US-NY':\n keys = ['NEW-YORK']\n direction = 1\n- elif sortedCountryCodes == 'CA-ON->US-MI':\n- keys = ['MICHIGAN']\n- direction = 1\n- elif sortedCountryCodes == 'CA-ON->US-MN':\n- keys = ['MINNESOTA']\n+ elif sortedCountryCodes == 'CA-ON->US-MISO':\n+ keys = ['MICHIGAN', 'MINNESOTA']\n direction = 1\n elif sortedCountryCodes == 'CA-ON->CA-QC':\n keys = filter(lambda k: k[:2] == 'PQ', exchanges.keys())\n"}}},{"rowIdx":10596,"cells":{"in_source_id":{"kind":"string","value":"ckan__ckan-5864"},"issue":{"kind":"string","value":"Embed file in another site problem with mime type\n**CKAN version**\r\nckan_version\": \"2.9\"\r\n\r\n**Describe the bug**\r\nEmbed file in another site from ckan force to download because mime type is incorrect, always is embed with Content-Type: application/octet-stream\r\n\r\n**Steps to reproduce**\r\nLoad PDF in ckan and use embed to display in another site, when you load page with embed file try download and not display pdf.\r\n\r\n**Expected behavior**\r\nDisplay embed pdf\r\n\n"},"before_files":{"kind":"list like","value":[{"content":"# encoding: utf-8\nimport cgi\nimport json\nimport logging\n\nimport flask\nfrom flask.views import MethodView\n\nimport six\nimport ckan.lib.base as base\nimport ckan.lib.datapreview as lib_datapreview\nimport ckan.lib.helpers as h\nimport ckan.lib.navl.dictization_functions as dict_fns\nimport ckan.lib.uploader as uploader\nimport ckan.logic as logic\nimport ckan.model as model\nimport ckan.plugins as plugins\nfrom ckan.common import _, g, request\nfrom ckan.views.home import CACHE_PARAMETERS\nfrom ckan.views.dataset import (\n _get_pkg_template, _get_package_type, _setup_template_variables\n)\n\nBlueprint = flask.Blueprint\nNotFound = logic.NotFound\nNotAuthorized = logic.NotAuthorized\nValidationError = logic.ValidationError\ncheck_access = logic.check_access\nget_action = logic.get_action\ntuplize_dict = logic.tuplize_dict\nclean_dict = logic.clean_dict\nparse_params = logic.parse_params\nflatten_to_string_key = logic.flatten_to_string_key\n\nlog = logging.getLogger(__name__)\n\nresource = Blueprint(\n u'dataset_resource',\n __name__,\n url_prefix=u'/dataset/<id>/resource',\n url_defaults={u'package_type': u'dataset'}\n)\nprefixed_resource = Blueprint(\n u'resource',\n __name__,\n url_prefix=u'/dataset/<id>/resource',\n url_defaults={u'package_type': u'dataset'}\n)\n\n\ndef read(package_type, id, resource_id):\n context = {\n u'model': model,\n u'session': model.Session,\n u'user': g.user,\n u'auth_user_obj': g.userobj,\n u'for_view': True\n }\n\n try:\n package = get_action(u'package_show')(context, {u'id': id})\n except (NotFound, NotAuthorized):\n return base.abort(404, _(u'Dataset not found'))\n activity_id = request.params.get(u'activity_id')\n if activity_id:\n # view an 'old' version of the package, as recorded in the\n # activity stream\n current_pkg = package\n try:\n package = context['session'].query(model.Activity).get(\n activity_id\n ).data['package']\n except AttributeError:\n base.abort(404, _(u'Dataset not found'))\n\n if package['id'] != current_pkg['id']:\n log.info(u'Mismatch between pkg id in activity and URL {} {}'\n .format(package['id'], current_pkg['id']))\n # the activity is not for the package in the URL - don't allow\n # misleading URLs as could be malicious\n base.abort(404, _(u'Activity not found'))\n # The name is used lots in the template for links, so fix it to be\n # the current one. It's not displayed to the user anyway.\n package['name'] = current_pkg['name']\n\n # Don't crash on old (unmigrated) activity records, which do not\n # include resources or extras.\n package.setdefault(u'resources', [])\n\n resource = None\n for res in package.get(u'resources', []):\n if res[u'id'] == resource_id:\n resource = res\n break\n if not resource:\n return base.abort(404, _(u'Resource not found'))\n\n # get package license info\n license_id = package.get(u'license_id')\n try:\n package[u'isopen'] = model.Package.get_license_register()[license_id\n ].isopen()\n except KeyError:\n package[u'isopen'] = False\n\n resource_views = get_action(u'resource_view_list')(\n context, {\n u'id': resource_id\n }\n )\n resource[u'has_views'] = len(resource_views) > 0\n\n current_resource_view = None\n view_id = request.args.get(u'view_id')\n if resource[u'has_views']:\n if view_id:\n current_resource_view = [\n rv for rv in resource_views if rv[u'id'] == view_id\n ]\n if len(current_resource_view) == 1:\n current_resource_view = current_resource_view[0]\n else:\n return base.abort(404, _(u'Resource view not found'))\n else:\n current_resource_view = resource_views[0]\n\n # required for nav menu\n pkg = context[u'package']\n dataset_type = pkg.type or package_type\n\n # TODO: remove\n g.package = package\n g.resource = resource\n g.pkg = pkg\n g.pkg_dict = package\n\n extra_vars = {\n u'resource_views': resource_views,\n u'current_resource_view': current_resource_view,\n u'dataset_type': dataset_type,\n u'pkg_dict': package,\n u'package': package,\n u'resource': resource,\n u'pkg': pkg, # NB it is the current version of the dataset, so ignores\n # activity_id. Still used though in resource views for\n # backward compatibility\n u'is_activity_archive': bool(activity_id),\n }\n\n template = _get_pkg_template(u'resource_template', dataset_type)\n return base.render(template, extra_vars)\n\n\ndef download(package_type, id, resource_id, filename=None):\n \"\"\"\n Provides a direct download by either redirecting the user to the url\n stored or downloading an uploaded file directly.\n \"\"\"\n context = {\n u'model': model,\n u'session': model.Session,\n u'user': g.user,\n u'auth_user_obj': g.userobj\n }\n\n try:\n rsc = get_action(u'resource_show')(context, {u'id': resource_id})\n get_action(u'package_show')(context, {u'id': id})\n except (NotFound, NotAuthorized):\n return base.abort(404, _(u'Resource not found'))\n\n if rsc.get(u'url_type') == u'upload':\n upload = uploader.get_resource_uploader(rsc)\n filepath = upload.get_path(rsc[u'id'])\n return flask.send_file(filepath)\n elif u'url' not in rsc:\n return base.abort(404, _(u'No download is available'))\n return h.redirect_to(rsc[u'url'])\n\n\nclass CreateView(MethodView):\n def post(self, package_type, id):\n save_action = request.form.get(u'save')\n data = clean_dict(\n dict_fns.unflatten(tuplize_dict(parse_params(request.form)))\n )\n data.update(clean_dict(\n dict_fns.unflatten(tuplize_dict(parse_params(request.files)))\n ))\n\n # we don't want to include save as it is part of the form\n del data[u'save']\n resource_id = data.pop(u'id')\n\n context = {\n u'model': model,\n u'session': model.Session,\n u'user': g.user,\n u'auth_user_obj': g.userobj\n }\n\n # see if we have any data that we are trying to save\n data_provided = False\n for key, value in six.iteritems(data):\n if (\n (value or isinstance(value, cgi.FieldStorage))\n and key != u'resource_type'):\n data_provided = True\n break\n\n if not data_provided and save_action != u\"go-dataset-complete\":\n if save_action == u'go-dataset':\n # go to final stage of adddataset\n return h.redirect_to(u'{}.edit'.format(package_type), id=id)\n # see if we have added any resources\n try:\n data_dict = get_action(u'package_show')(context, {u'id': id})\n except NotAuthorized:\n return base.abort(403, _(u'Unauthorized to update dataset'))\n except NotFound:\n return base.abort(\n 404,\n _(u'The dataset {id} could not be found.').format(id=id)\n )\n if not len(data_dict[u'resources']):\n # no data so keep on page\n msg = _(u'You must add at least one data resource')\n # On new templates do not use flash message\n\n errors = {}\n error_summary = {_(u'Error'): msg}\n return self.get(package_type, id, data, errors, error_summary)\n\n # XXX race condition if another user edits/deletes\n data_dict = get_action(u'package_show')(context, {u'id': id})\n get_action(u'package_update')(\n dict(context, allow_state_change=True),\n dict(data_dict, state=u'active')\n )\n return h.redirect_to(u'{}.read'.format(package_type), id=id)\n\n data[u'package_id'] = id\n try:\n if resource_id:\n data[u'id'] = resource_id\n get_action(u'resource_update')(context, data)\n else:\n get_action(u'resource_create')(context, data)\n except ValidationError as e:\n errors = e.error_dict\n error_summary = e.error_summary\n if data.get(u'url_type') == u'upload' and data.get(u'url'):\n data[u'url'] = u''\n data[u'url_type'] = u''\n data[u'previous_upload'] = True\n return self.get(package_type, id, data, errors, error_summary)\n except NotAuthorized:\n return base.abort(403, _(u'Unauthorized to create a resource'))\n except NotFound:\n return base.abort(\n 404, _(u'The dataset {id} could not be found.').format(id=id)\n )\n if save_action == u'go-metadata':\n # XXX race condition if another user edits/deletes\n data_dict = get_action(u'package_show')(context, {u'id': id})\n get_action(u'package_update')(\n dict(context, allow_state_change=True),\n dict(data_dict, state=u'active')\n )\n return h.redirect_to(u'{}.read'.format(package_type), id=id)\n elif save_action == u'go-dataset':\n # go to first stage of add dataset\n return h.redirect_to(u'{}.edit'.format(package_type), id=id)\n elif save_action == u'go-dataset-complete':\n\n return h.redirect_to(u'{}.read'.format(package_type), id=id)\n else:\n # add more resources\n return h.redirect_to(\n u'{}_resource.new'.format(package_type),\n id=id\n )\n\n def get(\n self, package_type, id, data=None, errors=None, error_summary=None\n ):\n # get resources for sidebar\n context = {\n u'model': model,\n u'session': model.Session,\n u'user': g.user,\n u'auth_user_obj': g.userobj\n }\n try:\n pkg_dict = get_action(u'package_show')(context, {u'id': id})\n except NotFound:\n return base.abort(\n 404, _(u'The dataset {id} could not be found.').format(id=id)\n )\n try:\n check_access(\n u'resource_create', context, {u\"package_id\": pkg_dict[\"id\"]}\n )\n except NotAuthorized:\n return base.abort(\n 403, _(u'Unauthorized to create a resource for this package')\n )\n\n package_type = pkg_dict[u'type'] or package_type\n\n errors = errors or {}\n error_summary = error_summary or {}\n extra_vars = {\n u'data': data,\n u'errors': errors,\n u'error_summary': error_summary,\n u'action': u'new',\n u'resource_form_snippet': _get_pkg_template(\n u'resource_form', package_type\n ),\n u'dataset_type': package_type,\n u'pkg_name': id,\n u'pkg_dict': pkg_dict\n }\n template = u'package/new_resource_not_draft.html'\n if pkg_dict[u'state'].startswith(u'draft'):\n extra_vars[u'stage'] = ['complete', u'active']\n template = u'package/new_resource.html'\n return base.render(template, extra_vars)\n\n\nclass EditView(MethodView):\n def _prepare(self, id):\n context = {\n u'model': model,\n u'session': model.Session,\n u'api_version': 3,\n u'for_edit': True,\n u'user': g.user,\n u'auth_user_obj': g.userobj\n }\n try:\n check_access(u'package_update', context, {u'id': id})\n except NotAuthorized:\n return base.abort(\n 403,\n _(u'User %r not authorized to edit %s') % (g.user, id)\n )\n return context\n\n def post(self, package_type, id, resource_id):\n context = self._prepare(id)\n data = clean_dict(\n dict_fns.unflatten(tuplize_dict(parse_params(request.form)))\n )\n data.update(clean_dict(\n dict_fns.unflatten(tuplize_dict(parse_params(request.files)))\n ))\n\n # we don't want to include save as it is part of the form\n del data[u'save']\n\n data[u'package_id'] = id\n try:\n if resource_id:\n data[u'id'] = resource_id\n get_action(u'resource_update')(context, data)\n else:\n get_action(u'resource_create')(context, data)\n except ValidationError as e:\n errors = e.error_dict\n error_summary = e.error_summary\n return self.get(\n package_type, id, resource_id, data, errors, error_summary\n )\n except NotAuthorized:\n return base.abort(403, _(u'Unauthorized to edit this resource'))\n return h.redirect_to(\n u'{}_resource.read'.format(package_type),\n id=id, resource_id=resource_id\n )\n\n def get(\n self,\n package_type,\n id,\n resource_id,\n data=None,\n errors=None,\n error_summary=None\n ):\n context = self._prepare(id)\n pkg_dict = get_action(u'package_show')(context, {u'id': id})\n\n try:\n resource_dict = get_action(u'resource_show')(\n context, {\n u'id': resource_id\n }\n )\n except NotFound:\n return base.abort(404, _(u'Resource not found'))\n\n if pkg_dict[u'state'].startswith(u'draft'):\n return CreateView().get(package_type, id, data=resource_dict)\n\n # resource is fully created\n resource = resource_dict\n # set the form action\n form_action = h.url_for(\n u'{}_resource.edit'.format(package_type),\n resource_id=resource_id, id=id\n )\n if not data:\n data = resource_dict\n\n package_type = pkg_dict[u'type'] or package_type\n\n errors = errors or {}\n error_summary = error_summary or {}\n extra_vars = {\n u'data': data,\n u'errors': errors,\n u'error_summary': error_summary,\n u'action': u'edit',\n u'resource_form_snippet': _get_pkg_template(\n u'resource_form', package_type\n ),\n u'dataset_type': package_type,\n u'resource': resource,\n u'pkg_dict': pkg_dict,\n u'form_action': form_action\n }\n return base.render(u'package/resource_edit.html', extra_vars)\n\n\nclass DeleteView(MethodView):\n def _prepare(self, id):\n context = {\n u'model': model,\n u'session': model.Session,\n u'user': g.user,\n u'auth_user_obj': g.userobj\n }\n try:\n check_access(u'package_delete', context, {u'id': id})\n except NotAuthorized:\n return base.abort(\n 403,\n _(u'Unauthorized to delete package %s') % u''\n )\n return context\n\n def post(self, package_type, id, resource_id):\n if u'cancel' in request.form:\n return h.redirect_to(\n u'{}_resource.edit'.format(package_type),\n resource_id=resource_id, id=id\n )\n context = self._prepare(id)\n\n try:\n get_action(u'resource_delete')(context, {u'id': resource_id})\n h.flash_notice(_(u'Resource has been deleted.'))\n pkg_dict = get_action(u'package_show')(None, {u'id': id})\n if pkg_dict[u'state'].startswith(u'draft'):\n return h.redirect_to(\n u'{}_resource.new'.format(package_type),\n id=id\n )\n else:\n return h.redirect_to(u'{}.read'.format(package_type), id=id)\n except NotAuthorized:\n return base.abort(\n 403,\n _(u'Unauthorized to delete resource %s') % u''\n )\n except NotFound:\n return base.abort(404, _(u'Resource not found'))\n\n def get(self, package_type, id, resource_id):\n context = self._prepare(id)\n try:\n resource_dict = get_action(u'resource_show')(\n context, {\n u'id': resource_id\n }\n )\n pkg_id = id\n except NotAuthorized:\n return base.abort(\n 403,\n _(u'Unauthorized to delete resource %s') % u''\n )\n except NotFound:\n return base.abort(404, _(u'Resource not found'))\n\n # TODO: remove\n g.resource_dict = resource_dict\n g.pkg_id = pkg_id\n\n return base.render(\n u'package/confirm_delete_resource.html', {\n u'dataset_type': _get_package_type(id),\n u'resource_dict': resource_dict,\n u'pkg_id': pkg_id\n }\n )\n\n\ndef views(package_type, id, resource_id):\n package_type = _get_package_type(id)\n context = {\n u'model': model,\n u'session': model.Session,\n u'user': g.user,\n u'for_view': True,\n u'auth_user_obj': g.userobj\n }\n data_dict = {u'id': id}\n\n try:\n check_access(u'package_update', context, data_dict)\n except NotAuthorized:\n return base.abort(\n 403,\n _(u'User %r not authorized to edit %s') % (g.user, id)\n )\n # check if package exists\n try:\n pkg_dict = get_action(u'package_show')(context, data_dict)\n pkg = context[u'package']\n except (NotFound, NotAuthorized):\n return base.abort(404, _(u'Dataset not found'))\n\n try:\n resource = get_action(u'resource_show')(context, {u'id': resource_id})\n views = get_action(u'resource_view_list')(\n context, {\n u'id': resource_id\n }\n )\n\n except NotFound:\n return base.abort(404, _(u'Resource not found'))\n except NotAuthorized:\n return base.abort(403, _(u'Unauthorized to read resource %s') % id)\n\n _setup_template_variables(context, {u'id': id}, package_type=package_type)\n\n # TODO: remove\n g.pkg_dict = pkg_dict\n g.pkg = pkg\n g.resource = resource\n g.views = views\n\n return base.render(\n u'package/resource_views.html', {\n u'pkg_dict': pkg_dict,\n u'pkg': pkg,\n u'resource': resource,\n u'views': views\n }\n )\n\n\ndef view(package_type, id, resource_id, view_id=None):\n \"\"\"\n Embedded page for a resource view.\n\n Depending on the type, different views are loaded. This could be an\n img tag where the image is loaded directly or an iframe that embeds a\n webpage or a recline preview.\n \"\"\"\n context = {\n u'model': model,\n u'session': model.Session,\n u'user': g.user,\n u'auth_user_obj': g.userobj\n }\n\n try:\n package = get_action(u'package_show')(context, {u'id': id})\n except (NotFound, NotAuthorized):\n return base.abort(404, _(u'Dataset not found'))\n\n try:\n resource = get_action(u'resource_show')(context, {u'id': resource_id})\n except (NotFound, NotAuthorized):\n return base.abort(404, _(u'Resource not found'))\n\n view = None\n if request.params.get(u'resource_view', u''):\n try:\n view = json.loads(request.params.get(u'resource_view', u''))\n except ValueError:\n return base.abort(409, _(u'Bad resource view data'))\n elif view_id:\n try:\n view = get_action(u'resource_view_show')(context, {u'id': view_id})\n except (NotFound, NotAuthorized):\n return base.abort(404, _(u'Resource view not found'))\n\n if not view or not isinstance(view, dict):\n return base.abort(404, _(u'Resource view not supplied'))\n\n return h.rendered_resource_view(view, resource, package, embed=True)\n\n\n# FIXME: could anyone think about better name?\nclass EditResourceViewView(MethodView):\n def _prepare(self, id, resource_id):\n context = {\n u'model': model,\n u'session': model.Session,\n u'user': g.user,\n u'for_view': True,\n u'auth_user_obj': g.userobj\n }\n\n # update resource should tell us early if the user has privilages.\n try:\n check_access(u'resource_update', context, {u'id': resource_id})\n except NotAuthorized:\n return base.abort(\n 403,\n _(u'User %r not authorized to edit %s') % (g.user, id)\n )\n\n # get resource and package data\n try:\n pkg_dict = get_action(u'package_show')(context, {u'id': id})\n pkg = context[u'package']\n except (NotFound, NotAuthorized):\n return base.abort(404, _(u'Dataset not found'))\n try:\n resource = get_action(u'resource_show')(\n context, {\n u'id': resource_id\n }\n )\n except (NotFound, NotAuthorized):\n return base.abort(404, _(u'Resource not found'))\n\n # TODO: remove\n g.pkg_dict = pkg_dict\n g.pkg = pkg\n g.resource = resource\n\n extra_vars = dict(\n data={},\n errors={},\n error_summary={},\n view_type=None,\n to_preview=False,\n pkg_dict=pkg_dict,\n pkg=pkg,\n resource=resource\n )\n return context, extra_vars\n\n def post(self, package_type, id, resource_id, view_id=None):\n context, extra_vars = self._prepare(id, resource_id)\n data = clean_dict(\n dict_fns.unflatten(\n tuplize_dict(\n parse_params(request.form, ignore_keys=CACHE_PARAMETERS)\n )\n )\n )\n data.pop(u'save', None)\n\n to_preview = data.pop(u'preview', False)\n if to_preview:\n context[u'preview'] = True\n to_delete = data.pop(u'delete', None)\n data[u'resource_id'] = resource_id\n data[u'view_type'] = request.args.get(u'view_type')\n\n try:\n if to_delete:\n data[u'id'] = view_id\n get_action(u'resource_view_delete')(context, data)\n elif view_id:\n data[u'id'] = view_id\n data = get_action(u'resource_view_update')(context, data)\n else:\n data = get_action(u'resource_view_create')(context, data)\n except ValidationError as e:\n # Could break preview if validation error\n to_preview = False\n extra_vars[u'errors'] = e.error_dict,\n extra_vars[u'error_summary'] = e.error_summary\n except NotAuthorized:\n # This should never happen unless the user maliciously changed\n # the resource_id in the url.\n return base.abort(403, _(u'Unauthorized to edit resource'))\n else:\n if not to_preview:\n return h.redirect_to(\n u'{}_resource.views'.format(package_type),\n id=id, resource_id=resource_id\n )\n extra_vars[u'data'] = data\n extra_vars[u'to_preview'] = to_preview\n return self.get(package_type, id, resource_id, view_id, extra_vars)\n\n def get(\n self, package_type, id, resource_id, view_id=None, post_extra=None\n ):\n context, extra_vars = self._prepare(id, resource_id)\n to_preview = extra_vars[u'to_preview']\n if post_extra:\n extra_vars.update(post_extra)\n\n package_type = _get_package_type(id)\n data = extra_vars[u'data'] if u'data' in extra_vars else None\n if data and u'view_type' in data:\n view_type = data.get(u'view_type')\n else:\n view_type = request.args.get(u'view_type')\n\n # view_id exists only when updating\n if view_id:\n if not data or not view_type:\n try:\n view_data = get_action(u'resource_view_show')(\n context, {\n u'id': view_id\n }\n )\n view_type = view_data[u'view_type']\n if data:\n data.update(view_data)\n else:\n data = view_data\n except (NotFound, NotAuthorized):\n return base.abort(404, _(u'View not found'))\n\n # might as well preview when loading good existing view\n if not extra_vars[u'errors']:\n to_preview = True\n\n data[u'view_type'] = view_type\n view_plugin = lib_datapreview.get_view_plugin(view_type)\n if not view_plugin:\n return base.abort(404, _(u'View Type Not found'))\n\n _setup_template_variables(\n context, {u'id': id}, package_type=package_type\n )\n\n data_dict = {\n u'package': extra_vars[u'pkg_dict'],\n u'resource': extra_vars[u'resource'],\n u'resource_view': data\n }\n\n view_template = view_plugin.view_template(context, data_dict)\n form_template = view_plugin.form_template(context, data_dict)\n\n extra_vars.update({\n u'form_template': form_template,\n u'view_template': view_template,\n u'data': data,\n u'to_preview': to_preview,\n u'datastore_available': plugins.plugin_loaded(u'datastore')\n })\n extra_vars.update(\n view_plugin.setup_template_variables(context, data_dict) or {}\n )\n extra_vars.update(data_dict)\n\n if view_id:\n return base.render(u'package/edit_view.html', extra_vars)\n\n return base.render(u'package/new_view.html', extra_vars)\n\n\ndef _parse_recline_state(params):\n state_version = int(request.args.get(u'state_version', u'1'))\n if state_version != 1:\n return None\n\n recline_state = {}\n for k, v in request.args.items():\n try:\n v = h.json.loads(v)\n except ValueError:\n pass\n recline_state[k] = v\n\n recline_state.pop(u'width', None)\n recline_state.pop(u'height', None)\n recline_state[u'readOnly'] = True\n\n # previous versions of recline setup used elasticsearch_url attribute\n # for data api url - see http://trac.ckan.org/ticket/2639\n # fix by relocating this to url attribute which is the default location\n if u'dataset' in recline_state and u'elasticsearch_url' in recline_state[\n u'dataset'\n ]:\n recline_state[u'dataset'][u'url'] = recline_state[u'dataset'][\n u'elasticsearch_url'\n ]\n\n # Ensure only the currentView is available\n # default to grid view if none specified\n if not recline_state.get(u'currentView', None):\n recline_state[u'currentView'] = u'grid'\n for k in recline_state.keys():\n if k.startswith(u'view-') and \\\n not k.endswith(recline_state[u'currentView']):\n recline_state.pop(k)\n return recline_state\n\n\ndef embedded_dataviewer(package_type, id, resource_id, width=500, height=500):\n \"\"\"\n Embedded page for a read-only resource dataview. Allows\n for width and height to be specified as part of the\n querystring (as well as accepting them via routes).\n \"\"\"\n context = {\n u'model': model,\n u'session': model.Session,\n u'user': g.user,\n u'auth_user_obj': g.userobj\n }\n\n try:\n resource = get_action(u'resource_show')(context, {u'id': resource_id})\n package = get_action(u'package_show')(context, {u'id': id})\n resource_json = h.json.dumps(resource)\n\n # double check that the resource belongs to the specified package\n if not resource[u'id'] in [r[u'id'] for r in package[u'resources']]:\n raise NotFound\n dataset_type = package[u'type'] or package_type\n\n except (NotFound, NotAuthorized):\n return base.abort(404, _(u'Resource not found'))\n\n # Construct the recline state\n state_version = int(request.args.get(u'state_version', u'1'))\n recline_state = _parse_recline_state(request.args)\n if recline_state is None:\n return base.abort(\n 400, (\n u'\"state\" parameter must be a valid recline '\n u'state (version %d)' % state_version\n )\n )\n\n recline_state = h.json.dumps(recline_state)\n\n width = max(int(request.args.get(u'width', width)), 100)\n height = max(int(request.args.get(u'height', height)), 100)\n embedded = True\n\n # TODO: remove\n g.resource = resource\n g.package = package\n g.resource_json = resource_json\n g.recline_state = recline_state\n g.width = width\n g.height = height\n g.embedded = embedded\n\n return base.render(\n u'package/resource_embedded_dataviewer.html', {\n u'dataset_type': dataset_type,\n u'resource': resource,\n u'package': package,\n u'resource_json': resource_json,\n u'width': width,\n u'height': height,\n u'embedded': embedded,\n u'recline_state': recline_state\n }\n )\n\n\ndef datapreview(package_type, id, resource_id):\n \"\"\"\n Embedded page for a resource data-preview.\n\n Depending on the type, different previews are loaded. This could be an\n img tag where the image is loaded directly or an iframe that embeds a\n webpage, or a recline preview.\n \"\"\"\n context = {\n u'model': model,\n u'session': model.Session,\n u'user': g.user,\n u'auth_user_obj': g.userobj\n }\n\n try:\n resource = get_action(u'resource_show')(context, {u'id': resource_id})\n package = get_action(u'package_show')(context, {u'id': id})\n\n data_dict = {u'resource': resource, u'package': package}\n\n preview_plugin = lib_datapreview.get_preview_plugin(data_dict)\n\n if preview_plugin is None:\n return base.abort(409, _(u'No preview has been defined.'))\n\n preview_plugin.setup_template_variables(context, data_dict)\n resource_json = json.dumps(resource)\n dataset_type = package[u'type'] or package_type\n\n # TODO: remove\n g.resource = resource\n g.package = package\n g.resource_json = resource_json\n\n except (NotFound, NotAuthorized):\n return base.abort(404, _(u'Resource not found'))\n else:\n return base.render(\n preview_plugin.preview_template(context, data_dict), {\n u'dataset_type': dataset_type,\n u'resource': resource,\n u'package': package,\n u'resource_json': resource_json\n }\n )\n\n\ndef register_dataset_plugin_rules(blueprint):\n blueprint.add_url_rule(u'/new', view_func=CreateView.as_view(str(u'new')))\n blueprint.add_url_rule(\n u'/<resource_id>', view_func=read, strict_slashes=False)\n blueprint.add_url_rule(\n u'/<resource_id>/edit', view_func=EditView.as_view(str(u'edit'))\n )\n blueprint.add_url_rule(\n u'/<resource_id>/delete', view_func=DeleteView.as_view(str(u'delete'))\n )\n\n blueprint.add_url_rule(u'/<resource_id>/download', view_func=download)\n blueprint.add_url_rule(u'/<resource_id>/views', view_func=views)\n blueprint.add_url_rule(u'/<resource_id>/view', view_func=view)\n blueprint.add_url_rule(u'/<resource_id>/view/<view_id>', view_func=view)\n blueprint.add_url_rule(\n u'/<resource_id>/download/<filename>', view_func=download\n )\n\n _edit_view = EditResourceViewView.as_view(str(u'edit_view'))\n blueprint.add_url_rule(u'/<resource_id>/new_view', view_func=_edit_view)\n blueprint.add_url_rule(\n u'/<resource_id>/edit_view/<view_id>', view_func=_edit_view\n )\n blueprint.add_url_rule(\n u'/<resource_id>/embed', view_func=embedded_dataviewer)\n blueprint.add_url_rule(\n u'/<resource_id>/viewer',\n view_func=embedded_dataviewer,\n defaults={\n u'width': u\"960\",\n u'height': u\"800\"\n }\n )\n blueprint.add_url_rule(u'/<resource_id>/preview', view_func=datapreview)\n\n\nregister_dataset_plugin_rules(resource)\nregister_dataset_plugin_rules(prefixed_resource)\n","path":"ckan/views/resource.py"}],"string":"[\n {\n \"content\": \"# encoding: utf-8\\nimport cgi\\nimport json\\nimport logging\\n\\nimport flask\\nfrom flask.views import MethodView\\n\\nimport six\\nimport ckan.lib.base as base\\nimport ckan.lib.datapreview as lib_datapreview\\nimport ckan.lib.helpers as h\\nimport ckan.lib.navl.dictization_functions as dict_fns\\nimport ckan.lib.uploader as uploader\\nimport ckan.logic as logic\\nimport ckan.model as model\\nimport ckan.plugins as plugins\\nfrom ckan.common import _, g, request\\nfrom ckan.views.home import CACHE_PARAMETERS\\nfrom ckan.views.dataset import (\\n _get_pkg_template, _get_package_type, _setup_template_variables\\n)\\n\\nBlueprint = flask.Blueprint\\nNotFound = logic.NotFound\\nNotAuthorized = logic.NotAuthorized\\nValidationError = logic.ValidationError\\ncheck_access = logic.check_access\\nget_action = logic.get_action\\ntuplize_dict = logic.tuplize_dict\\nclean_dict = logic.clean_dict\\nparse_params = logic.parse_params\\nflatten_to_string_key = logic.flatten_to_string_key\\n\\nlog = logging.getLogger(__name__)\\n\\nresource = Blueprint(\\n u'dataset_resource',\\n __name__,\\n url_prefix=u'/dataset/<id>/resource',\\n url_defaults={u'package_type': u'dataset'}\\n)\\nprefixed_resource = Blueprint(\\n u'resource',\\n __name__,\\n url_prefix=u'/dataset/<id>/resource',\\n url_defaults={u'package_type': u'dataset'}\\n)\\n\\n\\ndef read(package_type, id, resource_id):\\n context = {\\n u'model': model,\\n u'session': model.Session,\\n u'user': g.user,\\n u'auth_user_obj': g.userobj,\\n u'for_view': True\\n }\\n\\n try:\\n package = get_action(u'package_show')(context, {u'id': id})\\n except (NotFound, NotAuthorized):\\n return base.abort(404, _(u'Dataset not found'))\\n activity_id = request.params.get(u'activity_id')\\n if activity_id:\\n # view an 'old' version of the package, as recorded in the\\n # activity stream\\n current_pkg = package\\n try:\\n package = context['session'].query(model.Activity).get(\\n activity_id\\n ).data['package']\\n except AttributeError:\\n base.abort(404, _(u'Dataset not found'))\\n\\n if package['id'] != current_pkg['id']:\\n log.info(u'Mismatch between pkg id in activity and URL {} {}'\\n .format(package['id'], current_pkg['id']))\\n # the activity is not for the package in the URL - don't allow\\n # misleading URLs as could be malicious\\n base.abort(404, _(u'Activity not found'))\\n # The name is used lots in the template for links, so fix it to be\\n # the current one. It's not displayed to the user anyway.\\n package['name'] = current_pkg['name']\\n\\n # Don't crash on old (unmigrated) activity records, which do not\\n # include resources or extras.\\n package.setdefault(u'resources', [])\\n\\n resource = None\\n for res in package.get(u'resources', []):\\n if res[u'id'] == resource_id:\\n resource = res\\n break\\n if not resource:\\n return base.abort(404, _(u'Resource not found'))\\n\\n # get package license info\\n license_id = package.get(u'license_id')\\n try:\\n package[u'isopen'] = model.Package.get_license_register()[license_id\\n ].isopen()\\n except KeyError:\\n package[u'isopen'] = False\\n\\n resource_views = get_action(u'resource_view_list')(\\n context, {\\n u'id': resource_id\\n }\\n )\\n resource[u'has_views'] = len(resource_views) > 0\\n\\n current_resource_view = None\\n view_id = request.args.get(u'view_id')\\n if resource[u'has_views']:\\n if view_id:\\n current_resource_view = [\\n rv for rv in resource_views if rv[u'id'] == view_id\\n ]\\n if len(current_resource_view) == 1:\\n current_resource_view = current_resource_view[0]\\n else:\\n return base.abort(404, _(u'Resource view not found'))\\n else:\\n current_resource_view = resource_views[0]\\n\\n # required for nav menu\\n pkg = context[u'package']\\n dataset_type = pkg.type or package_type\\n\\n # TODO: remove\\n g.package = package\\n g.resource = resource\\n g.pkg = pkg\\n g.pkg_dict = package\\n\\n extra_vars = {\\n u'resource_views': resource_views,\\n u'current_resource_view': current_resource_view,\\n u'dataset_type': dataset_type,\\n u'pkg_dict': package,\\n u'package': package,\\n u'resource': resource,\\n u'pkg': pkg, # NB it is the current version of the dataset, so ignores\\n # activity_id. Still used though in resource views for\\n # backward compatibility\\n u'is_activity_archive': bool(activity_id),\\n }\\n\\n template = _get_pkg_template(u'resource_template', dataset_type)\\n return base.render(template, extra_vars)\\n\\n\\ndef download(package_type, id, resource_id, filename=None):\\n \\\"\\\"\\\"\\n Provides a direct download by either redirecting the user to the url\\n stored or downloading an uploaded file directly.\\n \\\"\\\"\\\"\\n context = {\\n u'model': model,\\n u'session': model.Session,\\n u'user': g.user,\\n u'auth_user_obj': g.userobj\\n }\\n\\n try:\\n rsc = get_action(u'resource_show')(context, {u'id': resource_id})\\n get_action(u'package_show')(context, {u'id': id})\\n except (NotFound, NotAuthorized):\\n return base.abort(404, _(u'Resource not found'))\\n\\n if rsc.get(u'url_type') == u'upload':\\n upload = uploader.get_resource_uploader(rsc)\\n filepath = upload.get_path(rsc[u'id'])\\n return flask.send_file(filepath)\\n elif u'url' not in rsc:\\n return base.abort(404, _(u'No download is available'))\\n return h.redirect_to(rsc[u'url'])\\n\\n\\nclass CreateView(MethodView):\\n def post(self, package_type, id):\\n save_action = request.form.get(u'save')\\n data = clean_dict(\\n dict_fns.unflatten(tuplize_dict(parse_params(request.form)))\\n )\\n data.update(clean_dict(\\n dict_fns.unflatten(tuplize_dict(parse_params(request.files)))\\n ))\\n\\n # we don't want to include save as it is part of the form\\n del data[u'save']\\n resource_id = data.pop(u'id')\\n\\n context = {\\n u'model': model,\\n u'session': model.Session,\\n u'user': g.user,\\n u'auth_user_obj': g.userobj\\n }\\n\\n # see if we have any data that we are trying to save\\n data_provided = False\\n for key, value in six.iteritems(data):\\n if (\\n (value or isinstance(value, cgi.FieldStorage))\\n and key != u'resource_type'):\\n data_provided = True\\n break\\n\\n if not data_provided and save_action != u\\\"go-dataset-complete\\\":\\n if save_action == u'go-dataset':\\n # go to final stage of adddataset\\n return h.redirect_to(u'{}.edit'.format(package_type), id=id)\\n # see if we have added any resources\\n try:\\n data_dict = get_action(u'package_show')(context, {u'id': id})\\n except NotAuthorized:\\n return base.abort(403, _(u'Unauthorized to update dataset'))\\n except NotFound:\\n return base.abort(\\n 404,\\n _(u'The dataset {id} could not be found.').format(id=id)\\n )\\n if not len(data_dict[u'resources']):\\n # no data so keep on page\\n msg = _(u'You must add at least one data resource')\\n # On new templates do not use flash message\\n\\n errors = {}\\n error_summary = {_(u'Error'): msg}\\n return self.get(package_type, id, data, errors, error_summary)\\n\\n # XXX race condition if another user edits/deletes\\n data_dict = get_action(u'package_show')(context, {u'id': id})\\n get_action(u'package_update')(\\n dict(context, allow_state_change=True),\\n dict(data_dict, state=u'active')\\n )\\n return h.redirect_to(u'{}.read'.format(package_type), id=id)\\n\\n data[u'package_id'] = id\\n try:\\n if resource_id:\\n data[u'id'] = resource_id\\n get_action(u'resource_update')(context, data)\\n else:\\n get_action(u'resource_create')(context, data)\\n except ValidationError as e:\\n errors = e.error_dict\\n error_summary = e.error_summary\\n if data.get(u'url_type') == u'upload' and data.get(u'url'):\\n data[u'url'] = u''\\n data[u'url_type'] = u''\\n data[u'previous_upload'] = True\\n return self.get(package_type, id, data, errors, error_summary)\\n except NotAuthorized:\\n return base.abort(403, _(u'Unauthorized to create a resource'))\\n except NotFound:\\n return base.abort(\\n 404, _(u'The dataset {id} could not be found.').format(id=id)\\n )\\n if save_action == u'go-metadata':\\n # XXX race condition if another user edits/deletes\\n data_dict = get_action(u'package_show')(context, {u'id': id})\\n get_action(u'package_update')(\\n dict(context, allow_state_change=True),\\n dict(data_dict, state=u'active')\\n )\\n return h.redirect_to(u'{}.read'.format(package_type), id=id)\\n elif save_action == u'go-dataset':\\n # go to first stage of add dataset\\n return h.redirect_to(u'{}.edit'.format(package_type), id=id)\\n elif save_action == u'go-dataset-complete':\\n\\n return h.redirect_to(u'{}.read'.format(package_type), id=id)\\n else:\\n # add more resources\\n return h.redirect_to(\\n u'{}_resource.new'.format(package_type),\\n id=id\\n )\\n\\n def get(\\n self, package_type, id, data=None, errors=None, error_summary=None\\n ):\\n # get resources for sidebar\\n context = {\\n u'model': model,\\n u'session': model.Session,\\n u'user': g.user,\\n u'auth_user_obj': g.userobj\\n }\\n try:\\n pkg_dict = get_action(u'package_show')(context, {u'id': id})\\n except NotFound:\\n return base.abort(\\n 404, _(u'The dataset {id} could not be found.').format(id=id)\\n )\\n try:\\n check_access(\\n u'resource_create', context, {u\\\"package_id\\\": pkg_dict[\\\"id\\\"]}\\n )\\n except NotAuthorized:\\n return base.abort(\\n 403, _(u'Unauthorized to create a resource for this package')\\n )\\n\\n package_type = pkg_dict[u'type'] or package_type\\n\\n errors = errors or {}\\n error_summary = error_summary or {}\\n extra_vars = {\\n u'data': data,\\n u'errors': errors,\\n u'error_summary': error_summary,\\n u'action': u'new',\\n u'resource_form_snippet': _get_pkg_template(\\n u'resource_form', package_type\\n ),\\n u'dataset_type': package_type,\\n u'pkg_name': id,\\n u'pkg_dict': pkg_dict\\n }\\n template = u'package/new_resource_not_draft.html'\\n if pkg_dict[u'state'].startswith(u'draft'):\\n extra_vars[u'stage'] = ['complete', u'active']\\n template = u'package/new_resource.html'\\n return base.render(template, extra_vars)\\n\\n\\nclass EditView(MethodView):\\n def _prepare(self, id):\\n context = {\\n u'model': model,\\n u'session': model.Session,\\n u'api_version': 3,\\n u'for_edit': True,\\n u'user': g.user,\\n u'auth_user_obj': g.userobj\\n }\\n try:\\n check_access(u'package_update', context, {u'id': id})\\n except NotAuthorized:\\n return base.abort(\\n 403,\\n _(u'User %r not authorized to edit %s') % (g.user, id)\\n )\\n return context\\n\\n def post(self, package_type, id, resource_id):\\n context = self._prepare(id)\\n data = clean_dict(\\n dict_fns.unflatten(tuplize_dict(parse_params(request.form)))\\n )\\n data.update(clean_dict(\\n dict_fns.unflatten(tuplize_dict(parse_params(request.files)))\\n ))\\n\\n # we don't want to include save as it is part of the form\\n del data[u'save']\\n\\n data[u'package_id'] = id\\n try:\\n if resource_id:\\n data[u'id'] = resource_id\\n get_action(u'resource_update')(context, data)\\n else:\\n get_action(u'resource_create')(context, data)\\n except ValidationError as e:\\n errors = e.error_dict\\n error_summary = e.error_summary\\n return self.get(\\n package_type, id, resource_id, data, errors, error_summary\\n )\\n except NotAuthorized:\\n return base.abort(403, _(u'Unauthorized to edit this resource'))\\n return h.redirect_to(\\n u'{}_resource.read'.format(package_type),\\n id=id, resource_id=resource_id\\n )\\n\\n def get(\\n self,\\n package_type,\\n id,\\n resource_id,\\n data=None,\\n errors=None,\\n error_summary=None\\n ):\\n context = self._prepare(id)\\n pkg_dict = get_action(u'package_show')(context, {u'id': id})\\n\\n try:\\n resource_dict = get_action(u'resource_show')(\\n context, {\\n u'id': resource_id\\n }\\n )\\n except NotFound:\\n return base.abort(404, _(u'Resource not found'))\\n\\n if pkg_dict[u'state'].startswith(u'draft'):\\n return CreateView().get(package_type, id, data=resource_dict)\\n\\n # resource is fully created\\n resource = resource_dict\\n # set the form action\\n form_action = h.url_for(\\n u'{}_resource.edit'.format(package_type),\\n resource_id=resource_id, id=id\\n )\\n if not data:\\n data = resource_dict\\n\\n package_type = pkg_dict[u'type'] or package_type\\n\\n errors = errors or {}\\n error_summary = error_summary or {}\\n extra_vars = {\\n u'data': data,\\n u'errors': errors,\\n u'error_summary': error_summary,\\n u'action': u'edit',\\n u'resource_form_snippet': _get_pkg_template(\\n u'resource_form', package_type\\n ),\\n u'dataset_type': package_type,\\n u'resource': resource,\\n u'pkg_dict': pkg_dict,\\n u'form_action': form_action\\n }\\n return base.render(u'package/resource_edit.html', extra_vars)\\n\\n\\nclass DeleteView(MethodView):\\n def _prepare(self, id):\\n context = {\\n u'model': model,\\n u'session': model.Session,\\n u'user': g.user,\\n u'auth_user_obj': g.userobj\\n }\\n try:\\n check_access(u'package_delete', context, {u'id': id})\\n except NotAuthorized:\\n return base.abort(\\n 403,\\n _(u'Unauthorized to delete package %s') % u''\\n )\\n return context\\n\\n def post(self, package_type, id, resource_id):\\n if u'cancel' in request.form:\\n return h.redirect_to(\\n u'{}_resource.edit'.format(package_type),\\n resource_id=resource_id, id=id\\n )\\n context = self._prepare(id)\\n\\n try:\\n get_action(u'resource_delete')(context, {u'id': resource_id})\\n h.flash_notice(_(u'Resource has been deleted.'))\\n pkg_dict = get_action(u'package_show')(None, {u'id': id})\\n if pkg_dict[u'state'].startswith(u'draft'):\\n return h.redirect_to(\\n u'{}_resource.new'.format(package_type),\\n id=id\\n )\\n else:\\n return h.redirect_to(u'{}.read'.format(package_type), id=id)\\n except NotAuthorized:\\n return base.abort(\\n 403,\\n _(u'Unauthorized to delete resource %s') % u''\\n )\\n except NotFound:\\n return base.abort(404, _(u'Resource not found'))\\n\\n def get(self, package_type, id, resource_id):\\n context = self._prepare(id)\\n try:\\n resource_dict = get_action(u'resource_show')(\\n context, {\\n u'id': resource_id\\n }\\n )\\n pkg_id = id\\n except NotAuthorized:\\n return base.abort(\\n 403,\\n _(u'Unauthorized to delete resource %s') % u''\\n )\\n except NotFound:\\n return base.abort(404, _(u'Resource not found'))\\n\\n # TODO: remove\\n g.resource_dict = resource_dict\\n g.pkg_id = pkg_id\\n\\n return base.render(\\n u'package/confirm_delete_resource.html', {\\n u'dataset_type': _get_package_type(id),\\n u'resource_dict': resource_dict,\\n u'pkg_id': pkg_id\\n }\\n )\\n\\n\\ndef views(package_type, id, resource_id):\\n package_type = _get_package_type(id)\\n context = {\\n u'model': model,\\n u'session': model.Session,\\n u'user': g.user,\\n u'for_view': True,\\n u'auth_user_obj': g.userobj\\n }\\n data_dict = {u'id': id}\\n\\n try:\\n check_access(u'package_update', context, data_dict)\\n except NotAuthorized:\\n return base.abort(\\n 403,\\n _(u'User %r not authorized to edit %s') % (g.user, id)\\n )\\n # check if package exists\\n try:\\n pkg_dict = get_action(u'package_show')(context, data_dict)\\n pkg = context[u'package']\\n except (NotFound, NotAuthorized):\\n return base.abort(404, _(u'Dataset not found'))\\n\\n try:\\n resource = get_action(u'resource_show')(context, {u'id': resource_id})\\n views = get_action(u'resource_view_list')(\\n context, {\\n u'id': resource_id\\n }\\n )\\n\\n except NotFound:\\n return base.abort(404, _(u'Resource not found'))\\n except NotAuthorized:\\n return base.abort(403, _(u'Unauthorized to read resource %s') % id)\\n\\n _setup_template_variables(context, {u'id': id}, package_type=package_type)\\n\\n # TODO: remove\\n g.pkg_dict = pkg_dict\\n g.pkg = pkg\\n g.resource = resource\\n g.views = views\\n\\n return base.render(\\n u'package/resource_views.html', {\\n u'pkg_dict': pkg_dict,\\n u'pkg': pkg,\\n u'resource': resource,\\n u'views': views\\n }\\n )\\n\\n\\ndef view(package_type, id, resource_id, view_id=None):\\n \\\"\\\"\\\"\\n Embedded page for a resource view.\\n\\n Depending on the type, different views are loaded. This could be an\\n img tag where the image is loaded directly or an iframe that embeds a\\n webpage or a recline preview.\\n \\\"\\\"\\\"\\n context = {\\n u'model': model,\\n u'session': model.Session,\\n u'user': g.user,\\n u'auth_user_obj': g.userobj\\n }\\n\\n try:\\n package = get_action(u'package_show')(context, {u'id': id})\\n except (NotFound, NotAuthorized):\\n return base.abort(404, _(u'Dataset not found'))\\n\\n try:\\n resource = get_action(u'resource_show')(context, {u'id': resource_id})\\n except (NotFound, NotAuthorized):\\n return base.abort(404, _(u'Resource not found'))\\n\\n view = None\\n if request.params.get(u'resource_view', u''):\\n try:\\n view = json.loads(request.params.get(u'resource_view', u''))\\n except ValueError:\\n return base.abort(409, _(u'Bad resource view data'))\\n elif view_id:\\n try:\\n view = get_action(u'resource_view_show')(context, {u'id': view_id})\\n except (NotFound, NotAuthorized):\\n return base.abort(404, _(u'Resource view not found'))\\n\\n if not view or not isinstance(view, dict):\\n return base.abort(404, _(u'Resource view not supplied'))\\n\\n return h.rendered_resource_view(view, resource, package, embed=True)\\n\\n\\n# FIXME: could anyone think about better name?\\nclass EditResourceViewView(MethodView):\\n def _prepare(self, id, resource_id):\\n context = {\\n u'model': model,\\n u'session': model.Session,\\n u'user': g.user,\\n u'for_view': True,\\n u'auth_user_obj': g.userobj\\n }\\n\\n # update resource should tell us early if the user has privilages.\\n try:\\n check_access(u'resource_update', context, {u'id': resource_id})\\n except NotAuthorized:\\n return base.abort(\\n 403,\\n _(u'User %r not authorized to edit %s') % (g.user, id)\\n )\\n\\n # get resource and package data\\n try:\\n pkg_dict = get_action(u'package_show')(context, {u'id': id})\\n pkg = context[u'package']\\n except (NotFound, NotAuthorized):\\n return base.abort(404, _(u'Dataset not found'))\\n try:\\n resource = get_action(u'resource_show')(\\n context, {\\n u'id': resource_id\\n }\\n )\\n except (NotFound, NotAuthorized):\\n return base.abort(404, _(u'Resource not found'))\\n\\n # TODO: remove\\n g.pkg_dict = pkg_dict\\n g.pkg = pkg\\n g.resource = resource\\n\\n extra_vars = dict(\\n data={},\\n errors={},\\n error_summary={},\\n view_type=None,\\n to_preview=False,\\n pkg_dict=pkg_dict,\\n pkg=pkg,\\n resource=resource\\n )\\n return context, extra_vars\\n\\n def post(self, package_type, id, resource_id, view_id=None):\\n context, extra_vars = self._prepare(id, resource_id)\\n data = clean_dict(\\n dict_fns.unflatten(\\n tuplize_dict(\\n parse_params(request.form, ignore_keys=CACHE_PARAMETERS)\\n )\\n )\\n )\\n data.pop(u'save', None)\\n\\n to_preview = data.pop(u'preview', False)\\n if to_preview:\\n context[u'preview'] = True\\n to_delete = data.pop(u'delete', None)\\n data[u'resource_id'] = resource_id\\n data[u'view_type'] = request.args.get(u'view_type')\\n\\n try:\\n if to_delete:\\n data[u'id'] = view_id\\n get_action(u'resource_view_delete')(context, data)\\n elif view_id:\\n data[u'id'] = view_id\\n data = get_action(u'resource_view_update')(context, data)\\n else:\\n data = get_action(u'resource_view_create')(context, data)\\n except ValidationError as e:\\n # Could break preview if validation error\\n to_preview = False\\n extra_vars[u'errors'] = e.error_dict,\\n extra_vars[u'error_summary'] = e.error_summary\\n except NotAuthorized:\\n # This should never happen unless the user maliciously changed\\n # the resource_id in the url.\\n return base.abort(403, _(u'Unauthorized to edit resource'))\\n else:\\n if not to_preview:\\n return h.redirect_to(\\n u'{}_resource.views'.format(package_type),\\n id=id, resource_id=resource_id\\n )\\n extra_vars[u'data'] = data\\n extra_vars[u'to_preview'] = to_preview\\n return self.get(package_type, id, resource_id, view_id, extra_vars)\\n\\n def get(\\n self, package_type, id, resource_id, view_id=None, post_extra=None\\n ):\\n context, extra_vars = self._prepare(id, resource_id)\\n to_preview = extra_vars[u'to_preview']\\n if post_extra:\\n extra_vars.update(post_extra)\\n\\n package_type = _get_package_type(id)\\n data = extra_vars[u'data'] if u'data' in extra_vars else None\\n if data and u'view_type' in data:\\n view_type = data.get(u'view_type')\\n else:\\n view_type = request.args.get(u'view_type')\\n\\n # view_id exists only when updating\\n if view_id:\\n if not data or not view_type:\\n try:\\n view_data = get_action(u'resource_view_show')(\\n context, {\\n u'id': view_id\\n }\\n )\\n view_type = view_data[u'view_type']\\n if data:\\n data.update(view_data)\\n else:\\n data = view_data\\n except (NotFound, NotAuthorized):\\n return base.abort(404, _(u'View not found'))\\n\\n # might as well preview when loading good existing view\\n if not extra_vars[u'errors']:\\n to_preview = True\\n\\n data[u'view_type'] = view_type\\n view_plugin = lib_datapreview.get_view_plugin(view_type)\\n if not view_plugin:\\n return base.abort(404, _(u'View Type Not found'))\\n\\n _setup_template_variables(\\n context, {u'id': id}, package_type=package_type\\n )\\n\\n data_dict = {\\n u'package': extra_vars[u'pkg_dict'],\\n u'resource': extra_vars[u'resource'],\\n u'resource_view': data\\n }\\n\\n view_template = view_plugin.view_template(context, data_dict)\\n form_template = view_plugin.form_template(context, data_dict)\\n\\n extra_vars.update({\\n u'form_template': form_template,\\n u'view_template': view_template,\\n u'data': data,\\n u'to_preview': to_preview,\\n u'datastore_available': plugins.plugin_loaded(u'datastore')\\n })\\n extra_vars.update(\\n view_plugin.setup_template_variables(context, data_dict) or {}\\n )\\n extra_vars.update(data_dict)\\n\\n if view_id:\\n return base.render(u'package/edit_view.html', extra_vars)\\n\\n return base.render(u'package/new_view.html', extra_vars)\\n\\n\\ndef _parse_recline_state(params):\\n state_version = int(request.args.get(u'state_version', u'1'))\\n if state_version != 1:\\n return None\\n\\n recline_state = {}\\n for k, v in request.args.items():\\n try:\\n v = h.json.loads(v)\\n except ValueError:\\n pass\\n recline_state[k] = v\\n\\n recline_state.pop(u'width', None)\\n recline_state.pop(u'height', None)\\n recline_state[u'readOnly'] = True\\n\\n # previous versions of recline setup used elasticsearch_url attribute\\n # for data api url - see http://trac.ckan.org/ticket/2639\\n # fix by relocating this to url attribute which is the default location\\n if u'dataset' in recline_state and u'elasticsearch_url' in recline_state[\\n u'dataset'\\n ]:\\n recline_state[u'dataset'][u'url'] = recline_state[u'dataset'][\\n u'elasticsearch_url'\\n ]\\n\\n # Ensure only the currentView is available\\n # default to grid view if none specified\\n if not recline_state.get(u'currentView', None):\\n recline_state[u'currentView'] = u'grid'\\n for k in recline_state.keys():\\n if k.startswith(u'view-') and \\\\\\n not k.endswith(recline_state[u'currentView']):\\n recline_state.pop(k)\\n return recline_state\\n\\n\\ndef embedded_dataviewer(package_type, id, resource_id, width=500, height=500):\\n \\\"\\\"\\\"\\n Embedded page for a read-only resource dataview. Allows\\n for width and height to be specified as part of the\\n querystring (as well as accepting them via routes).\\n \\\"\\\"\\\"\\n context = {\\n u'model': model,\\n u'session': model.Session,\\n u'user': g.user,\\n u'auth_user_obj': g.userobj\\n }\\n\\n try:\\n resource = get_action(u'resource_show')(context, {u'id': resource_id})\\n package = get_action(u'package_show')(context, {u'id': id})\\n resource_json = h.json.dumps(resource)\\n\\n # double check that the resource belongs to the specified package\\n if not resource[u'id'] in [r[u'id'] for r in package[u'resources']]:\\n raise NotFound\\n dataset_type = package[u'type'] or package_type\\n\\n except (NotFound, NotAuthorized):\\n return base.abort(404, _(u'Resource not found'))\\n\\n # Construct the recline state\\n state_version = int(request.args.get(u'state_version', u'1'))\\n recline_state = _parse_recline_state(request.args)\\n if recline_state is None:\\n return base.abort(\\n 400, (\\n u'\\\"state\\\" parameter must be a valid recline '\\n u'state (version %d)' % state_version\\n )\\n )\\n\\n recline_state = h.json.dumps(recline_state)\\n\\n width = max(int(request.args.get(u'width', width)), 100)\\n height = max(int(request.args.get(u'height', height)), 100)\\n embedded = True\\n\\n # TODO: remove\\n g.resource = resource\\n g.package = package\\n g.resource_json = resource_json\\n g.recline_state = recline_state\\n g.width = width\\n g.height = height\\n g.embedded = embedded\\n\\n return base.render(\\n u'package/resource_embedded_dataviewer.html', {\\n u'dataset_type': dataset_type,\\n u'resource': resource,\\n u'package': package,\\n u'resource_json': resource_json,\\n u'width': width,\\n u'height': height,\\n u'embedded': embedded,\\n u'recline_state': recline_state\\n }\\n )\\n\\n\\ndef datapreview(package_type, id, resource_id):\\n \\\"\\\"\\\"\\n Embedded page for a resource data-preview.\\n\\n Depending on the type, different previews are loaded. This could be an\\n img tag where the image is loaded directly or an iframe that embeds a\\n webpage, or a recline preview.\\n \\\"\\\"\\\"\\n context = {\\n u'model': model,\\n u'session': model.Session,\\n u'user': g.user,\\n u'auth_user_obj': g.userobj\\n }\\n\\n try:\\n resource = get_action(u'resource_show')(context, {u'id': resource_id})\\n package = get_action(u'package_show')(context, {u'id': id})\\n\\n data_dict = {u'resource': resource, u'package': package}\\n\\n preview_plugin = lib_datapreview.get_preview_plugin(data_dict)\\n\\n if preview_plugin is None:\\n return base.abort(409, _(u'No preview has been defined.'))\\n\\n preview_plugin.setup_template_variables(context, data_dict)\\n resource_json = json.dumps(resource)\\n dataset_type = package[u'type'] or package_type\\n\\n # TODO: remove\\n g.resource = resource\\n g.package = package\\n g.resource_json = resource_json\\n\\n except (NotFound, NotAuthorized):\\n return base.abort(404, _(u'Resource not found'))\\n else:\\n return base.render(\\n preview_plugin.preview_template(context, data_dict), {\\n u'dataset_type': dataset_type,\\n u'resource': resource,\\n u'package': package,\\n u'resource_json': resource_json\\n }\\n )\\n\\n\\ndef register_dataset_plugin_rules(blueprint):\\n blueprint.add_url_rule(u'/new', view_func=CreateView.as_view(str(u'new')))\\n blueprint.add_url_rule(\\n u'/<resource_id>', view_func=read, strict_slashes=False)\\n blueprint.add_url_rule(\\n u'/<resource_id>/edit', view_func=EditView.as_view(str(u'edit'))\\n )\\n blueprint.add_url_rule(\\n u'/<resource_id>/delete', view_func=DeleteView.as_view(str(u'delete'))\\n )\\n\\n blueprint.add_url_rule(u'/<resource_id>/download', view_func=download)\\n blueprint.add_url_rule(u'/<resource_id>/views', view_func=views)\\n blueprint.add_url_rule(u'/<resource_id>/view', view_func=view)\\n blueprint.add_url_rule(u'/<resource_id>/view/<view_id>', view_func=view)\\n blueprint.add_url_rule(\\n u'/<resource_id>/download/<filename>', view_func=download\\n )\\n\\n _edit_view = EditResourceViewView.as_view(str(u'edit_view'))\\n blueprint.add_url_rule(u'/<resource_id>/new_view', view_func=_edit_view)\\n blueprint.add_url_rule(\\n u'/<resource_id>/edit_view/<view_id>', view_func=_edit_view\\n )\\n blueprint.add_url_rule(\\n u'/<resource_id>/embed', view_func=embedded_dataviewer)\\n blueprint.add_url_rule(\\n u'/<resource_id>/viewer',\\n view_func=embedded_dataviewer,\\n defaults={\\n u'width': u\\\"960\\\",\\n u'height': u\\\"800\\\"\\n }\\n )\\n blueprint.add_url_rule(u'/<resource_id>/preview', view_func=datapreview)\\n\\n\\nregister_dataset_plugin_rules(resource)\\nregister_dataset_plugin_rules(prefixed_resource)\\n\",\n \"path\": \"ckan/views/resource.py\"\n }\n]","isConversational":false},"after_files":{"kind":"list like","value":[{"content":"# encoding: utf-8\nimport cgi\nimport json\nimport logging\n\nimport flask\nfrom flask.views import MethodView\n\nimport six\nimport ckan.lib.base as base\nimport ckan.lib.datapreview as lib_datapreview\nimport ckan.lib.helpers as h\nimport ckan.lib.navl.dictization_functions as dict_fns\nimport ckan.lib.uploader as uploader\nimport ckan.logic as logic\nimport ckan.model as model\nimport ckan.plugins as plugins\nfrom ckan.common import _, g, request\nfrom ckan.views.home import CACHE_PARAMETERS\nfrom ckan.views.dataset import (\n _get_pkg_template, _get_package_type, _setup_template_variables\n)\n\nBlueprint = flask.Blueprint\nNotFound = logic.NotFound\nNotAuthorized = logic.NotAuthorized\nValidationError = logic.ValidationError\ncheck_access = logic.check_access\nget_action = logic.get_action\ntuplize_dict = logic.tuplize_dict\nclean_dict = logic.clean_dict\nparse_params = logic.parse_params\nflatten_to_string_key = logic.flatten_to_string_key\n\nlog = logging.getLogger(__name__)\n\nresource = Blueprint(\n u'dataset_resource',\n __name__,\n url_prefix=u'/dataset/<id>/resource',\n url_defaults={u'package_type': u'dataset'}\n)\nprefixed_resource = Blueprint(\n u'resource',\n __name__,\n url_prefix=u'/dataset/<id>/resource',\n url_defaults={u'package_type': u'dataset'}\n)\n\n\ndef read(package_type, id, resource_id):\n context = {\n u'model': model,\n u'session': model.Session,\n u'user': g.user,\n u'auth_user_obj': g.userobj,\n u'for_view': True\n }\n\n try:\n package = get_action(u'package_show')(context, {u'id': id})\n except (NotFound, NotAuthorized):\n return base.abort(404, _(u'Dataset not found'))\n activity_id = request.params.get(u'activity_id')\n if activity_id:\n # view an 'old' version of the package, as recorded in the\n # activity stream\n current_pkg = package\n try:\n package = context['session'].query(model.Activity).get(\n activity_id\n ).data['package']\n except AttributeError:\n base.abort(404, _(u'Dataset not found'))\n\n if package['id'] != current_pkg['id']:\n log.info(u'Mismatch between pkg id in activity and URL {} {}'\n .format(package['id'], current_pkg['id']))\n # the activity is not for the package in the URL - don't allow\n # misleading URLs as could be malicious\n base.abort(404, _(u'Activity not found'))\n # The name is used lots in the template for links, so fix it to be\n # the current one. It's not displayed to the user anyway.\n package['name'] = current_pkg['name']\n\n # Don't crash on old (unmigrated) activity records, which do not\n # include resources or extras.\n package.setdefault(u'resources', [])\n\n resource = None\n for res in package.get(u'resources', []):\n if res[u'id'] == resource_id:\n resource = res\n break\n if not resource:\n return base.abort(404, _(u'Resource not found'))\n\n # get package license info\n license_id = package.get(u'license_id')\n try:\n package[u'isopen'] = model.Package.get_license_register()[license_id\n ].isopen()\n except KeyError:\n package[u'isopen'] = False\n\n resource_views = get_action(u'resource_view_list')(\n context, {\n u'id': resource_id\n }\n )\n resource[u'has_views'] = len(resource_views) > 0\n\n current_resource_view = None\n view_id = request.args.get(u'view_id')\n if resource[u'has_views']:\n if view_id:\n current_resource_view = [\n rv for rv in resource_views if rv[u'id'] == view_id\n ]\n if len(current_resource_view) == 1:\n current_resource_view = current_resource_view[0]\n else:\n return base.abort(404, _(u'Resource view not found'))\n else:\n current_resource_view = resource_views[0]\n\n # required for nav menu\n pkg = context[u'package']\n dataset_type = pkg.type or package_type\n\n # TODO: remove\n g.package = package\n g.resource = resource\n g.pkg = pkg\n g.pkg_dict = package\n\n extra_vars = {\n u'resource_views': resource_views,\n u'current_resource_view': current_resource_view,\n u'dataset_type': dataset_type,\n u'pkg_dict': package,\n u'package': package,\n u'resource': resource,\n u'pkg': pkg, # NB it is the current version of the dataset, so ignores\n # activity_id. Still used though in resource views for\n # backward compatibility\n u'is_activity_archive': bool(activity_id),\n }\n\n template = _get_pkg_template(u'resource_template', dataset_type)\n return base.render(template, extra_vars)\n\n\ndef download(package_type, id, resource_id, filename=None):\n \"\"\"\n Provides a direct download by either redirecting the user to the url\n stored or downloading an uploaded file directly.\n \"\"\"\n context = {\n u'model': model,\n u'session': model.Session,\n u'user': g.user,\n u'auth_user_obj': g.userobj\n }\n\n try:\n rsc = get_action(u'resource_show')(context, {u'id': resource_id})\n get_action(u'package_show')(context, {u'id': id})\n except (NotFound, NotAuthorized):\n return base.abort(404, _(u'Resource not found'))\n\n if rsc.get(u'url_type') == u'upload':\n upload = uploader.get_resource_uploader(rsc)\n filepath = upload.get_path(rsc[u'id'])\n resp = flask.send_file(filepath)\n if rsc.get('mimetype'):\n resp.headers['Content-Type'] = rsc['mimetype']\n return resp\n\n elif u'url' not in rsc:\n return base.abort(404, _(u'No download is available'))\n return h.redirect_to(rsc[u'url'])\n\n\nclass CreateView(MethodView):\n def post(self, package_type, id):\n save_action = request.form.get(u'save')\n data = clean_dict(\n dict_fns.unflatten(tuplize_dict(parse_params(request.form)))\n )\n data.update(clean_dict(\n dict_fns.unflatten(tuplize_dict(parse_params(request.files)))\n ))\n\n # we don't want to include save as it is part of the form\n del data[u'save']\n resource_id = data.pop(u'id')\n\n context = {\n u'model': model,\n u'session': model.Session,\n u'user': g.user,\n u'auth_user_obj': g.userobj\n }\n\n # see if we have any data that we are trying to save\n data_provided = False\n for key, value in six.iteritems(data):\n if (\n (value or isinstance(value, cgi.FieldStorage))\n and key != u'resource_type'):\n data_provided = True\n break\n\n if not data_provided and save_action != u\"go-dataset-complete\":\n if save_action == u'go-dataset':\n # go to final stage of adddataset\n return h.redirect_to(u'{}.edit'.format(package_type), id=id)\n # see if we have added any resources\n try:\n data_dict = get_action(u'package_show')(context, {u'id': id})\n except NotAuthorized:\n return base.abort(403, _(u'Unauthorized to update dataset'))\n except NotFound:\n return base.abort(\n 404,\n _(u'The dataset {id} could not be found.').format(id=id)\n )\n if not len(data_dict[u'resources']):\n # no data so keep on page\n msg = _(u'You must add at least one data resource')\n # On new templates do not use flash message\n\n errors = {}\n error_summary = {_(u'Error'): msg}\n return self.get(package_type, id, data, errors, error_summary)\n\n # XXX race condition if another user edits/deletes\n data_dict = get_action(u'package_show')(context, {u'id': id})\n get_action(u'package_update')(\n dict(context, allow_state_change=True),\n dict(data_dict, state=u'active')\n )\n return h.redirect_to(u'{}.read'.format(package_type), id=id)\n\n data[u'package_id'] = id\n try:\n if resource_id:\n data[u'id'] = resource_id\n get_action(u'resource_update')(context, data)\n else:\n get_action(u'resource_create')(context, data)\n except ValidationError as e:\n errors = e.error_dict\n error_summary = e.error_summary\n if data.get(u'url_type') == u'upload' and data.get(u'url'):\n data[u'url'] = u''\n data[u'url_type'] = u''\n data[u'previous_upload'] = True\n return self.get(package_type, id, data, errors, error_summary)\n except NotAuthorized:\n return base.abort(403, _(u'Unauthorized to create a resource'))\n except NotFound:\n return base.abort(\n 404, _(u'The dataset {id} could not be found.').format(id=id)\n )\n if save_action == u'go-metadata':\n # XXX race condition if another user edits/deletes\n data_dict = get_action(u'package_show')(context, {u'id': id})\n get_action(u'package_update')(\n dict(context, allow_state_change=True),\n dict(data_dict, state=u'active')\n )\n return h.redirect_to(u'{}.read'.format(package_type), id=id)\n elif save_action == u'go-dataset':\n # go to first stage of add dataset\n return h.redirect_to(u'{}.edit'.format(package_type), id=id)\n elif save_action == u'go-dataset-complete':\n\n return h.redirect_to(u'{}.read'.format(package_type), id=id)\n else:\n # add more resources\n return h.redirect_to(\n u'{}_resource.new'.format(package_type),\n id=id\n )\n\n def get(\n self, package_type, id, data=None, errors=None, error_summary=None\n ):\n # get resources for sidebar\n context = {\n u'model': model,\n u'session': model.Session,\n u'user': g.user,\n u'auth_user_obj': g.userobj\n }\n try:\n pkg_dict = get_action(u'package_show')(context, {u'id': id})\n except NotFound:\n return base.abort(\n 404, _(u'The dataset {id} could not be found.').format(id=id)\n )\n try:\n check_access(\n u'resource_create', context, {u\"package_id\": pkg_dict[\"id\"]}\n )\n except NotAuthorized:\n return base.abort(\n 403, _(u'Unauthorized to create a resource for this package')\n )\n\n package_type = pkg_dict[u'type'] or package_type\n\n errors = errors or {}\n error_summary = error_summary or {}\n extra_vars = {\n u'data': data,\n u'errors': errors,\n u'error_summary': error_summary,\n u'action': u'new',\n u'resource_form_snippet': _get_pkg_template(\n u'resource_form', package_type\n ),\n u'dataset_type': package_type,\n u'pkg_name': id,\n u'pkg_dict': pkg_dict\n }\n template = u'package/new_resource_not_draft.html'\n if pkg_dict[u'state'].startswith(u'draft'):\n extra_vars[u'stage'] = ['complete', u'active']\n template = u'package/new_resource.html'\n return base.render(template, extra_vars)\n\n\nclass EditView(MethodView):\n def _prepare(self, id):\n context = {\n u'model': model,\n u'session': model.Session,\n u'api_version': 3,\n u'for_edit': True,\n u'user': g.user,\n u'auth_user_obj': g.userobj\n }\n try:\n check_access(u'package_update', context, {u'id': id})\n except NotAuthorized:\n return base.abort(\n 403,\n _(u'User %r not authorized to edit %s') % (g.user, id)\n )\n return context\n\n def post(self, package_type, id, resource_id):\n context = self._prepare(id)\n data = clean_dict(\n dict_fns.unflatten(tuplize_dict(parse_params(request.form)))\n )\n data.update(clean_dict(\n dict_fns.unflatten(tuplize_dict(parse_params(request.files)))\n ))\n\n # we don't want to include save as it is part of the form\n del data[u'save']\n\n data[u'package_id'] = id\n try:\n if resource_id:\n data[u'id'] = resource_id\n get_action(u'resource_update')(context, data)\n else:\n get_action(u'resource_create')(context, data)\n except ValidationError as e:\n errors = e.error_dict\n error_summary = e.error_summary\n return self.get(\n package_type, id, resource_id, data, errors, error_summary\n )\n except NotAuthorized:\n return base.abort(403, _(u'Unauthorized to edit this resource'))\n return h.redirect_to(\n u'{}_resource.read'.format(package_type),\n id=id, resource_id=resource_id\n )\n\n def get(\n self,\n package_type,\n id,\n resource_id,\n data=None,\n errors=None,\n error_summary=None\n ):\n context = self._prepare(id)\n pkg_dict = get_action(u'package_show')(context, {u'id': id})\n\n try:\n resource_dict = get_action(u'resource_show')(\n context, {\n u'id': resource_id\n }\n )\n except NotFound:\n return base.abort(404, _(u'Resource not found'))\n\n if pkg_dict[u'state'].startswith(u'draft'):\n return CreateView().get(package_type, id, data=resource_dict)\n\n # resource is fully created\n resource = resource_dict\n # set the form action\n form_action = h.url_for(\n u'{}_resource.edit'.format(package_type),\n resource_id=resource_id, id=id\n )\n if not data:\n data = resource_dict\n\n package_type = pkg_dict[u'type'] or package_type\n\n errors = errors or {}\n error_summary = error_summary or {}\n extra_vars = {\n u'data': data,\n u'errors': errors,\n u'error_summary': error_summary,\n u'action': u'edit',\n u'resource_form_snippet': _get_pkg_template(\n u'resource_form', package_type\n ),\n u'dataset_type': package_type,\n u'resource': resource,\n u'pkg_dict': pkg_dict,\n u'form_action': form_action\n }\n return base.render(u'package/resource_edit.html', extra_vars)\n\n\nclass DeleteView(MethodView):\n def _prepare(self, id):\n context = {\n u'model': model,\n u'session': model.Session,\n u'user': g.user,\n u'auth_user_obj': g.userobj\n }\n try:\n check_access(u'package_delete', context, {u'id': id})\n except NotAuthorized:\n return base.abort(\n 403,\n _(u'Unauthorized to delete package %s') % u''\n )\n return context\n\n def post(self, package_type, id, resource_id):\n if u'cancel' in request.form:\n return h.redirect_to(\n u'{}_resource.edit'.format(package_type),\n resource_id=resource_id, id=id\n )\n context = self._prepare(id)\n\n try:\n get_action(u'resource_delete')(context, {u'id': resource_id})\n h.flash_notice(_(u'Resource has been deleted.'))\n pkg_dict = get_action(u'package_show')(None, {u'id': id})\n if pkg_dict[u'state'].startswith(u'draft'):\n return h.redirect_to(\n u'{}_resource.new'.format(package_type),\n id=id\n )\n else:\n return h.redirect_to(u'{}.read'.format(package_type), id=id)\n except NotAuthorized:\n return base.abort(\n 403,\n _(u'Unauthorized to delete resource %s') % u''\n )\n except NotFound:\n return base.abort(404, _(u'Resource not found'))\n\n def get(self, package_type, id, resource_id):\n context = self._prepare(id)\n try:\n resource_dict = get_action(u'resource_show')(\n context, {\n u'id': resource_id\n }\n )\n pkg_id = id\n except NotAuthorized:\n return base.abort(\n 403,\n _(u'Unauthorized to delete resource %s') % u''\n )\n except NotFound:\n return base.abort(404, _(u'Resource not found'))\n\n # TODO: remove\n g.resource_dict = resource_dict\n g.pkg_id = pkg_id\n\n return base.render(\n u'package/confirm_delete_resource.html', {\n u'dataset_type': _get_package_type(id),\n u'resource_dict': resource_dict,\n u'pkg_id': pkg_id\n }\n )\n\n\ndef views(package_type, id, resource_id):\n package_type = _get_package_type(id)\n context = {\n u'model': model,\n u'session': model.Session,\n u'user': g.user,\n u'for_view': True,\n u'auth_user_obj': g.userobj\n }\n data_dict = {u'id': id}\n\n try:\n check_access(u'package_update', context, data_dict)\n except NotAuthorized:\n return base.abort(\n 403,\n _(u'User %r not authorized to edit %s') % (g.user, id)\n )\n # check if package exists\n try:\n pkg_dict = get_action(u'package_show')(context, data_dict)\n pkg = context[u'package']\n except (NotFound, NotAuthorized):\n return base.abort(404, _(u'Dataset not found'))\n\n try:\n resource = get_action(u'resource_show')(context, {u'id': resource_id})\n views = get_action(u'resource_view_list')(\n context, {\n u'id': resource_id\n }\n )\n\n except NotFound:\n return base.abort(404, _(u'Resource not found'))\n except NotAuthorized:\n return base.abort(403, _(u'Unauthorized to read resource %s') % id)\n\n _setup_template_variables(context, {u'id': id}, package_type=package_type)\n\n # TODO: remove\n g.pkg_dict = pkg_dict\n g.pkg = pkg\n g.resource = resource\n g.views = views\n\n return base.render(\n u'package/resource_views.html', {\n u'pkg_dict': pkg_dict,\n u'pkg': pkg,\n u'resource': resource,\n u'views': views\n }\n )\n\n\ndef view(package_type, id, resource_id, view_id=None):\n \"\"\"\n Embedded page for a resource view.\n\n Depending on the type, different views are loaded. This could be an\n img tag where the image is loaded directly or an iframe that embeds a\n webpage or a recline preview.\n \"\"\"\n context = {\n u'model': model,\n u'session': model.Session,\n u'user': g.user,\n u'auth_user_obj': g.userobj\n }\n\n try:\n package = get_action(u'package_show')(context, {u'id': id})\n except (NotFound, NotAuthorized):\n return base.abort(404, _(u'Dataset not found'))\n\n try:\n resource = get_action(u'resource_show')(context, {u'id': resource_id})\n except (NotFound, NotAuthorized):\n return base.abort(404, _(u'Resource not found'))\n\n view = None\n if request.params.get(u'resource_view', u''):\n try:\n view = json.loads(request.params.get(u'resource_view', u''))\n except ValueError:\n return base.abort(409, _(u'Bad resource view data'))\n elif view_id:\n try:\n view = get_action(u'resource_view_show')(context, {u'id': view_id})\n except (NotFound, NotAuthorized):\n return base.abort(404, _(u'Resource view not found'))\n\n if not view or not isinstance(view, dict):\n return base.abort(404, _(u'Resource view not supplied'))\n\n return h.rendered_resource_view(view, resource, package, embed=True)\n\n\n# FIXME: could anyone think about better name?\nclass EditResourceViewView(MethodView):\n def _prepare(self, id, resource_id):\n context = {\n u'model': model,\n u'session': model.Session,\n u'user': g.user,\n u'for_view': True,\n u'auth_user_obj': g.userobj\n }\n\n # update resource should tell us early if the user has privilages.\n try:\n check_access(u'resource_update', context, {u'id': resource_id})\n except NotAuthorized:\n return base.abort(\n 403,\n _(u'User %r not authorized to edit %s') % (g.user, id)\n )\n\n # get resource and package data\n try:\n pkg_dict = get_action(u'package_show')(context, {u'id': id})\n pkg = context[u'package']\n except (NotFound, NotAuthorized):\n return base.abort(404, _(u'Dataset not found'))\n try:\n resource = get_action(u'resource_show')(\n context, {\n u'id': resource_id\n }\n )\n except (NotFound, NotAuthorized):\n return base.abort(404, _(u'Resource not found'))\n\n # TODO: remove\n g.pkg_dict = pkg_dict\n g.pkg = pkg\n g.resource = resource\n\n extra_vars = dict(\n data={},\n errors={},\n error_summary={},\n view_type=None,\n to_preview=False,\n pkg_dict=pkg_dict,\n pkg=pkg,\n resource=resource\n )\n return context, extra_vars\n\n def post(self, package_type, id, resource_id, view_id=None):\n context, extra_vars = self._prepare(id, resource_id)\n data = clean_dict(\n dict_fns.unflatten(\n tuplize_dict(\n parse_params(request.form, ignore_keys=CACHE_PARAMETERS)\n )\n )\n )\n data.pop(u'save', None)\n\n to_preview = data.pop(u'preview', False)\n if to_preview:\n context[u'preview'] = True\n to_delete = data.pop(u'delete', None)\n data[u'resource_id'] = resource_id\n data[u'view_type'] = request.args.get(u'view_type')\n\n try:\n if to_delete:\n data[u'id'] = view_id\n get_action(u'resource_view_delete')(context, data)\n elif view_id:\n data[u'id'] = view_id\n data = get_action(u'resource_view_update')(context, data)\n else:\n data = get_action(u'resource_view_create')(context, data)\n except ValidationError as e:\n # Could break preview if validation error\n to_preview = False\n extra_vars[u'errors'] = e.error_dict,\n extra_vars[u'error_summary'] = e.error_summary\n except NotAuthorized:\n # This should never happen unless the user maliciously changed\n # the resource_id in the url.\n return base.abort(403, _(u'Unauthorized to edit resource'))\n else:\n if not to_preview:\n return h.redirect_to(\n u'{}_resource.views'.format(package_type),\n id=id, resource_id=resource_id\n )\n extra_vars[u'data'] = data\n extra_vars[u'to_preview'] = to_preview\n return self.get(package_type, id, resource_id, view_id, extra_vars)\n\n def get(\n self, package_type, id, resource_id, view_id=None, post_extra=None\n ):\n context, extra_vars = self._prepare(id, resource_id)\n to_preview = extra_vars[u'to_preview']\n if post_extra:\n extra_vars.update(post_extra)\n\n package_type = _get_package_type(id)\n data = extra_vars[u'data'] if u'data' in extra_vars else None\n if data and u'view_type' in data:\n view_type = data.get(u'view_type')\n else:\n view_type = request.args.get(u'view_type')\n\n # view_id exists only when updating\n if view_id:\n if not data or not view_type:\n try:\n view_data = get_action(u'resource_view_show')(\n context, {\n u'id': view_id\n }\n )\n view_type = view_data[u'view_type']\n if data:\n data.update(view_data)\n else:\n data = view_data\n except (NotFound, NotAuthorized):\n return base.abort(404, _(u'View not found'))\n\n # might as well preview when loading good existing view\n if not extra_vars[u'errors']:\n to_preview = True\n\n data[u'view_type'] = view_type\n view_plugin = lib_datapreview.get_view_plugin(view_type)\n if not view_plugin:\n return base.abort(404, _(u'View Type Not found'))\n\n _setup_template_variables(\n context, {u'id': id}, package_type=package_type\n )\n\n data_dict = {\n u'package': extra_vars[u'pkg_dict'],\n u'resource': extra_vars[u'resource'],\n u'resource_view': data\n }\n\n view_template = view_plugin.view_template(context, data_dict)\n form_template = view_plugin.form_template(context, data_dict)\n\n extra_vars.update({\n u'form_template': form_template,\n u'view_template': view_template,\n u'data': data,\n u'to_preview': to_preview,\n u'datastore_available': plugins.plugin_loaded(u'datastore')\n })\n extra_vars.update(\n view_plugin.setup_template_variables(context, data_dict) or {}\n )\n extra_vars.update(data_dict)\n\n if view_id:\n return base.render(u'package/edit_view.html', extra_vars)\n\n return base.render(u'package/new_view.html', extra_vars)\n\n\ndef _parse_recline_state(params):\n state_version = int(request.args.get(u'state_version', u'1'))\n if state_version != 1:\n return None\n\n recline_state = {}\n for k, v in request.args.items():\n try:\n v = h.json.loads(v)\n except ValueError:\n pass\n recline_state[k] = v\n\n recline_state.pop(u'width', None)\n recline_state.pop(u'height', None)\n recline_state[u'readOnly'] = True\n\n # previous versions of recline setup used elasticsearch_url attribute\n # for data api url - see http://trac.ckan.org/ticket/2639\n # fix by relocating this to url attribute which is the default location\n if u'dataset' in recline_state and u'elasticsearch_url' in recline_state[\n u'dataset'\n ]:\n recline_state[u'dataset'][u'url'] = recline_state[u'dataset'][\n u'elasticsearch_url'\n ]\n\n # Ensure only the currentView is available\n # default to grid view if none specified\n if not recline_state.get(u'currentView', None):\n recline_state[u'currentView'] = u'grid'\n for k in recline_state.keys():\n if k.startswith(u'view-') and \\\n not k.endswith(recline_state[u'currentView']):\n recline_state.pop(k)\n return recline_state\n\n\ndef embedded_dataviewer(package_type, id, resource_id, width=500, height=500):\n \"\"\"\n Embedded page for a read-only resource dataview. Allows\n for width and height to be specified as part of the\n querystring (as well as accepting them via routes).\n \"\"\"\n context = {\n u'model': model,\n u'session': model.Session,\n u'user': g.user,\n u'auth_user_obj': g.userobj\n }\n\n try:\n resource = get_action(u'resource_show')(context, {u'id': resource_id})\n package = get_action(u'package_show')(context, {u'id': id})\n resource_json = h.json.dumps(resource)\n\n # double check that the resource belongs to the specified package\n if not resource[u'id'] in [r[u'id'] for r in package[u'resources']]:\n raise NotFound\n dataset_type = package[u'type'] or package_type\n\n except (NotFound, NotAuthorized):\n return base.abort(404, _(u'Resource not found'))\n\n # Construct the recline state\n state_version = int(request.args.get(u'state_version', u'1'))\n recline_state = _parse_recline_state(request.args)\n if recline_state is None:\n return base.abort(\n 400, (\n u'\"state\" parameter must be a valid recline '\n u'state (version %d)' % state_version\n )\n )\n\n recline_state = h.json.dumps(recline_state)\n\n width = max(int(request.args.get(u'width', width)), 100)\n height = max(int(request.args.get(u'height', height)), 100)\n embedded = True\n\n # TODO: remove\n g.resource = resource\n g.package = package\n g.resource_json = resource_json\n g.recline_state = recline_state\n g.width = width\n g.height = height\n g.embedded = embedded\n\n return base.render(\n u'package/resource_embedded_dataviewer.html', {\n u'dataset_type': dataset_type,\n u'resource': resource,\n u'package': package,\n u'resource_json': resource_json,\n u'width': width,\n u'height': height,\n u'embedded': embedded,\n u'recline_state': recline_state\n }\n )\n\n\ndef datapreview(package_type, id, resource_id):\n \"\"\"\n Embedded page for a resource data-preview.\n\n Depending on the type, different previews are loaded. This could be an\n img tag where the image is loaded directly or an iframe that embeds a\n webpage, or a recline preview.\n \"\"\"\n context = {\n u'model': model,\n u'session': model.Session,\n u'user': g.user,\n u'auth_user_obj': g.userobj\n }\n\n try:\n resource = get_action(u'resource_show')(context, {u'id': resource_id})\n package = get_action(u'package_show')(context, {u'id': id})\n\n data_dict = {u'resource': resource, u'package': package}\n\n preview_plugin = lib_datapreview.get_preview_plugin(data_dict)\n\n if preview_plugin is None:\n return base.abort(409, _(u'No preview has been defined.'))\n\n preview_plugin.setup_template_variables(context, data_dict)\n resource_json = json.dumps(resource)\n dataset_type = package[u'type'] or package_type\n\n # TODO: remove\n g.resource = resource\n g.package = package\n g.resource_json = resource_json\n\n except (NotFound, NotAuthorized):\n return base.abort(404, _(u'Resource not found'))\n else:\n return base.render(\n preview_plugin.preview_template(context, data_dict), {\n u'dataset_type': dataset_type,\n u'resource': resource,\n u'package': package,\n u'resource_json': resource_json\n }\n )\n\n\ndef register_dataset_plugin_rules(blueprint):\n blueprint.add_url_rule(u'/new', view_func=CreateView.as_view(str(u'new')))\n blueprint.add_url_rule(\n u'/<resource_id>', view_func=read, strict_slashes=False)\n blueprint.add_url_rule(\n u'/<resource_id>/edit', view_func=EditView.as_view(str(u'edit'))\n )\n blueprint.add_url_rule(\n u'/<resource_id>/delete', view_func=DeleteView.as_view(str(u'delete'))\n )\n\n blueprint.add_url_rule(u'/<resource_id>/download', view_func=download)\n blueprint.add_url_rule(u'/<resource_id>/views', view_func=views)\n blueprint.add_url_rule(u'/<resource_id>/view', view_func=view)\n blueprint.add_url_rule(u'/<resource_id>/view/<view_id>', view_func=view)\n blueprint.add_url_rule(\n u'/<resource_id>/download/<filename>', view_func=download\n )\n\n _edit_view = EditResourceViewView.as_view(str(u'edit_view'))\n blueprint.add_url_rule(u'/<resource_id>/new_view', view_func=_edit_view)\n blueprint.add_url_rule(\n u'/<resource_id>/edit_view/<view_id>', view_func=_edit_view\n )\n blueprint.add_url_rule(\n u'/<resource_id>/embed', view_func=embedded_dataviewer)\n blueprint.add_url_rule(\n u'/<resource_id>/viewer',\n view_func=embedded_dataviewer,\n defaults={\n u'width': u\"960\",\n u'height': u\"800\"\n }\n )\n blueprint.add_url_rule(u'/<resource_id>/preview', view_func=datapreview)\n\n\nregister_dataset_plugin_rules(resource)\nregister_dataset_plugin_rules(prefixed_resource)\n","path":"ckan/views/resource.py"}],"string":"[\n {\n \"content\": \"# encoding: utf-8\\nimport cgi\\nimport json\\nimport logging\\n\\nimport flask\\nfrom flask.views import MethodView\\n\\nimport six\\nimport ckan.lib.base as base\\nimport ckan.lib.datapreview as lib_datapreview\\nimport ckan.lib.helpers as h\\nimport ckan.lib.navl.dictization_functions as dict_fns\\nimport ckan.lib.uploader as uploader\\nimport ckan.logic as logic\\nimport ckan.model as model\\nimport ckan.plugins as plugins\\nfrom ckan.common import _, g, request\\nfrom ckan.views.home import CACHE_PARAMETERS\\nfrom ckan.views.dataset import (\\n _get_pkg_template, _get_package_type, _setup_template_variables\\n)\\n\\nBlueprint = flask.Blueprint\\nNotFound = logic.NotFound\\nNotAuthorized = logic.NotAuthorized\\nValidationError = logic.ValidationError\\ncheck_access = logic.check_access\\nget_action = logic.get_action\\ntuplize_dict = logic.tuplize_dict\\nclean_dict = logic.clean_dict\\nparse_params = logic.parse_params\\nflatten_to_string_key = logic.flatten_to_string_key\\n\\nlog = logging.getLogger(__name__)\\n\\nresource = Blueprint(\\n u'dataset_resource',\\n __name__,\\n url_prefix=u'/dataset/<id>/resource',\\n url_defaults={u'package_type': u'dataset'}\\n)\\nprefixed_resource = Blueprint(\\n u'resource',\\n __name__,\\n url_prefix=u'/dataset/<id>/resource',\\n url_defaults={u'package_type': u'dataset'}\\n)\\n\\n\\ndef read(package_type, id, resource_id):\\n context = {\\n u'model': model,\\n u'session': model.Session,\\n u'user': g.user,\\n u'auth_user_obj': g.userobj,\\n u'for_view': True\\n }\\n\\n try:\\n package = get_action(u'package_show')(context, {u'id': id})\\n except (NotFound, NotAuthorized):\\n return base.abort(404, _(u'Dataset not found'))\\n activity_id = request.params.get(u'activity_id')\\n if activity_id:\\n # view an 'old' version of the package, as recorded in the\\n # activity stream\\n current_pkg = package\\n try:\\n package = context['session'].query(model.Activity).get(\\n activity_id\\n ).data['package']\\n except AttributeError:\\n base.abort(404, _(u'Dataset not found'))\\n\\n if package['id'] != current_pkg['id']:\\n log.info(u'Mismatch between pkg id in activity and URL {} {}'\\n .format(package['id'], current_pkg['id']))\\n # the activity is not for the package in the URL - don't allow\\n # misleading URLs as could be malicious\\n base.abort(404, _(u'Activity not found'))\\n # The name is used lots in the template for links, so fix it to be\\n # the current one. It's not displayed to the user anyway.\\n package['name'] = current_pkg['name']\\n\\n # Don't crash on old (unmigrated) activity records, which do not\\n # include resources or extras.\\n package.setdefault(u'resources', [])\\n\\n resource = None\\n for res in package.get(u'resources', []):\\n if res[u'id'] == resource_id:\\n resource = res\\n break\\n if not resource:\\n return base.abort(404, _(u'Resource not found'))\\n\\n # get package license info\\n license_id = package.get(u'license_id')\\n try:\\n package[u'isopen'] = model.Package.get_license_register()[license_id\\n ].isopen()\\n except KeyError:\\n package[u'isopen'] = False\\n\\n resource_views = get_action(u'resource_view_list')(\\n context, {\\n u'id': resource_id\\n }\\n )\\n resource[u'has_views'] = len(resource_views) > 0\\n\\n current_resource_view = None\\n view_id = request.args.get(u'view_id')\\n if resource[u'has_views']:\\n if view_id:\\n current_resource_view = [\\n rv for rv in resource_views if rv[u'id'] == view_id\\n ]\\n if len(current_resource_view) == 1:\\n current_resource_view = current_resource_view[0]\\n else:\\n return base.abort(404, _(u'Resource view not found'))\\n else:\\n current_resource_view = resource_views[0]\\n\\n # required for nav menu\\n pkg = context[u'package']\\n dataset_type = pkg.type or package_type\\n\\n # TODO: remove\\n g.package = package\\n g.resource = resource\\n g.pkg = pkg\\n g.pkg_dict = package\\n\\n extra_vars = {\\n u'resource_views': resource_views,\\n u'current_resource_view': current_resource_view,\\n u'dataset_type': dataset_type,\\n u'pkg_dict': package,\\n u'package': package,\\n u'resource': resource,\\n u'pkg': pkg, # NB it is the current version of the dataset, so ignores\\n # activity_id. Still used though in resource views for\\n # backward compatibility\\n u'is_activity_archive': bool(activity_id),\\n }\\n\\n template = _get_pkg_template(u'resource_template', dataset_type)\\n return base.render(template, extra_vars)\\n\\n\\ndef download(package_type, id, resource_id, filename=None):\\n \\\"\\\"\\\"\\n Provides a direct download by either redirecting the user to the url\\n stored or downloading an uploaded file directly.\\n \\\"\\\"\\\"\\n context = {\\n u'model': model,\\n u'session': model.Session,\\n u'user': g.user,\\n u'auth_user_obj': g.userobj\\n }\\n\\n try:\\n rsc = get_action(u'resource_show')(context, {u'id': resource_id})\\n get_action(u'package_show')(context, {u'id': id})\\n except (NotFound, NotAuthorized):\\n return base.abort(404, _(u'Resource not found'))\\n\\n if rsc.get(u'url_type') == u'upload':\\n upload = uploader.get_resource_uploader(rsc)\\n filepath = upload.get_path(rsc[u'id'])\\n resp = flask.send_file(filepath)\\n if rsc.get('mimetype'):\\n resp.headers['Content-Type'] = rsc['mimetype']\\n return resp\\n\\n elif u'url' not in rsc:\\n return base.abort(404, _(u'No download is available'))\\n return h.redirect_to(rsc[u'url'])\\n\\n\\nclass CreateView(MethodView):\\n def post(self, package_type, id):\\n save_action = request.form.get(u'save')\\n data = clean_dict(\\n dict_fns.unflatten(tuplize_dict(parse_params(request.form)))\\n )\\n data.update(clean_dict(\\n dict_fns.unflatten(tuplize_dict(parse_params(request.files)))\\n ))\\n\\n # we don't want to include save as it is part of the form\\n del data[u'save']\\n resource_id = data.pop(u'id')\\n\\n context = {\\n u'model': model,\\n u'session': model.Session,\\n u'user': g.user,\\n u'auth_user_obj': g.userobj\\n }\\n\\n # see if we have any data that we are trying to save\\n data_provided = False\\n for key, value in six.iteritems(data):\\n if (\\n (value or isinstance(value, cgi.FieldStorage))\\n and key != u'resource_type'):\\n data_provided = True\\n break\\n\\n if not data_provided and save_action != u\\\"go-dataset-complete\\\":\\n if save_action == u'go-dataset':\\n # go to final stage of adddataset\\n return h.redirect_to(u'{}.edit'.format(package_type), id=id)\\n # see if we have added any resources\\n try:\\n data_dict = get_action(u'package_show')(context, {u'id': id})\\n except NotAuthorized:\\n return base.abort(403, _(u'Unauthorized to update dataset'))\\n except NotFound:\\n return base.abort(\\n 404,\\n _(u'The dataset {id} could not be found.').format(id=id)\\n )\\n if not len(data_dict[u'resources']):\\n # no data so keep on page\\n msg = _(u'You must add at least one data resource')\\n # On new templates do not use flash message\\n\\n errors = {}\\n error_summary = {_(u'Error'): msg}\\n return self.get(package_type, id, data, errors, error_summary)\\n\\n # XXX race condition if another user edits/deletes\\n data_dict = get_action(u'package_show')(context, {u'id': id})\\n get_action(u'package_update')(\\n dict(context, allow_state_change=True),\\n dict(data_dict, state=u'active')\\n )\\n return h.redirect_to(u'{}.read'.format(package_type), id=id)\\n\\n data[u'package_id'] = id\\n try:\\n if resource_id:\\n data[u'id'] = resource_id\\n get_action(u'resource_update')(context, data)\\n else:\\n get_action(u'resource_create')(context, data)\\n except ValidationError as e:\\n errors = e.error_dict\\n error_summary = e.error_summary\\n if data.get(u'url_type') == u'upload' and data.get(u'url'):\\n data[u'url'] = u''\\n data[u'url_type'] = u''\\n data[u'previous_upload'] = True\\n return self.get(package_type, id, data, errors, error_summary)\\n except NotAuthorized:\\n return base.abort(403, _(u'Unauthorized to create a resource'))\\n except NotFound:\\n return base.abort(\\n 404, _(u'The dataset {id} could not be found.').format(id=id)\\n )\\n if save_action == u'go-metadata':\\n # XXX race condition if another user edits/deletes\\n data_dict = get_action(u'package_show')(context, {u'id': id})\\n get_action(u'package_update')(\\n dict(context, allow_state_change=True),\\n dict(data_dict, state=u'active')\\n )\\n return h.redirect_to(u'{}.read'.format(package_type), id=id)\\n elif save_action == u'go-dataset':\\n # go to first stage of add dataset\\n return h.redirect_to(u'{}.edit'.format(package_type), id=id)\\n elif save_action == u'go-dataset-complete':\\n\\n return h.redirect_to(u'{}.read'.format(package_type), id=id)\\n else:\\n # add more resources\\n return h.redirect_to(\\n u'{}_resource.new'.format(package_type),\\n id=id\\n )\\n\\n def get(\\n self, package_type, id, data=None, errors=None, error_summary=None\\n ):\\n # get resources for sidebar\\n context = {\\n u'model': model,\\n u'session': model.Session,\\n u'user': g.user,\\n u'auth_user_obj': g.userobj\\n }\\n try:\\n pkg_dict = get_action(u'package_show')(context, {u'id': id})\\n except NotFound:\\n return base.abort(\\n 404, _(u'The dataset {id} could not be found.').format(id=id)\\n )\\n try:\\n check_access(\\n u'resource_create', context, {u\\\"package_id\\\": pkg_dict[\\\"id\\\"]}\\n )\\n except NotAuthorized:\\n return base.abort(\\n 403, _(u'Unauthorized to create a resource for this package')\\n )\\n\\n package_type = pkg_dict[u'type'] or package_type\\n\\n errors = errors or {}\\n error_summary = error_summary or {}\\n extra_vars = {\\n u'data': data,\\n u'errors': errors,\\n u'error_summary': error_summary,\\n u'action': u'new',\\n u'resource_form_snippet': _get_pkg_template(\\n u'resource_form', package_type\\n ),\\n u'dataset_type': package_type,\\n u'pkg_name': id,\\n u'pkg_dict': pkg_dict\\n }\\n template = u'package/new_resource_not_draft.html'\\n if pkg_dict[u'state'].startswith(u'draft'):\\n extra_vars[u'stage'] = ['complete', u'active']\\n template = u'package/new_resource.html'\\n return base.render(template, extra_vars)\\n\\n\\nclass EditView(MethodView):\\n def _prepare(self, id):\\n context = {\\n u'model': model,\\n u'session': model.Session,\\n u'api_version': 3,\\n u'for_edit': True,\\n u'user': g.user,\\n u'auth_user_obj': g.userobj\\n }\\n try:\\n check_access(u'package_update', context, {u'id': id})\\n except NotAuthorized:\\n return base.abort(\\n 403,\\n _(u'User %r not authorized to edit %s') % (g.user, id)\\n )\\n return context\\n\\n def post(self, package_type, id, resource_id):\\n context = self._prepare(id)\\n data = clean_dict(\\n dict_fns.unflatten(tuplize_dict(parse_params(request.form)))\\n )\\n data.update(clean_dict(\\n dict_fns.unflatten(tuplize_dict(parse_params(request.files)))\\n ))\\n\\n # we don't want to include save as it is part of the form\\n del data[u'save']\\n\\n data[u'package_id'] = id\\n try:\\n if resource_id:\\n data[u'id'] = resource_id\\n get_action(u'resource_update')(context, data)\\n else:\\n get_action(u'resource_create')(context, data)\\n except ValidationError as e:\\n errors = e.error_dict\\n error_summary = e.error_summary\\n return self.get(\\n package_type, id, resource_id, data, errors, error_summary\\n )\\n except NotAuthorized:\\n return base.abort(403, _(u'Unauthorized to edit this resource'))\\n return h.redirect_to(\\n u'{}_resource.read'.format(package_type),\\n id=id, resource_id=resource_id\\n )\\n\\n def get(\\n self,\\n package_type,\\n id,\\n resource_id,\\n data=None,\\n errors=None,\\n error_summary=None\\n ):\\n context = self._prepare(id)\\n pkg_dict = get_action(u'package_show')(context, {u'id': id})\\n\\n try:\\n resource_dict = get_action(u'resource_show')(\\n context, {\\n u'id': resource_id\\n }\\n )\\n except NotFound:\\n return base.abort(404, _(u'Resource not found'))\\n\\n if pkg_dict[u'state'].startswith(u'draft'):\\n return CreateView().get(package_type, id, data=resource_dict)\\n\\n # resource is fully created\\n resource = resource_dict\\n # set the form action\\n form_action = h.url_for(\\n u'{}_resource.edit'.format(package_type),\\n resource_id=resource_id, id=id\\n )\\n if not data:\\n data = resource_dict\\n\\n package_type = pkg_dict[u'type'] or package_type\\n\\n errors = errors or {}\\n error_summary = error_summary or {}\\n extra_vars = {\\n u'data': data,\\n u'errors': errors,\\n u'error_summary': error_summary,\\n u'action': u'edit',\\n u'resource_form_snippet': _get_pkg_template(\\n u'resource_form', package_type\\n ),\\n u'dataset_type': package_type,\\n u'resource': resource,\\n u'pkg_dict': pkg_dict,\\n u'form_action': form_action\\n }\\n return base.render(u'package/resource_edit.html', extra_vars)\\n\\n\\nclass DeleteView(MethodView):\\n def _prepare(self, id):\\n context = {\\n u'model': model,\\n u'session': model.Session,\\n u'user': g.user,\\n u'auth_user_obj': g.userobj\\n }\\n try:\\n check_access(u'package_delete', context, {u'id': id})\\n except NotAuthorized:\\n return base.abort(\\n 403,\\n _(u'Unauthorized to delete package %s') % u''\\n )\\n return context\\n\\n def post(self, package_type, id, resource_id):\\n if u'cancel' in request.form:\\n return h.redirect_to(\\n u'{}_resource.edit'.format(package_type),\\n resource_id=resource_id, id=id\\n )\\n context = self._prepare(id)\\n\\n try:\\n get_action(u'resource_delete')(context, {u'id': resource_id})\\n h.flash_notice(_(u'Resource has been deleted.'))\\n pkg_dict = get_action(u'package_show')(None, {u'id': id})\\n if pkg_dict[u'state'].startswith(u'draft'):\\n return h.redirect_to(\\n u'{}_resource.new'.format(package_type),\\n id=id\\n )\\n else:\\n return h.redirect_to(u'{}.read'.format(package_type), id=id)\\n except NotAuthorized:\\n return base.abort(\\n 403,\\n _(u'Unauthorized to delete resource %s') % u''\\n )\\n except NotFound:\\n return base.abort(404, _(u'Resource not found'))\\n\\n def get(self, package_type, id, resource_id):\\n context = self._prepare(id)\\n try:\\n resource_dict = get_action(u'resource_show')(\\n context, {\\n u'id': resource_id\\n }\\n )\\n pkg_id = id\\n except NotAuthorized:\\n return base.abort(\\n 403,\\n _(u'Unauthorized to delete resource %s') % u''\\n )\\n except NotFound:\\n return base.abort(404, _(u'Resource not found'))\\n\\n # TODO: remove\\n g.resource_dict = resource_dict\\n g.pkg_id = pkg_id\\n\\n return base.render(\\n u'package/confirm_delete_resource.html', {\\n u'dataset_type': _get_package_type(id),\\n u'resource_dict': resource_dict,\\n u'pkg_id': pkg_id\\n }\\n )\\n\\n\\ndef views(package_type, id, resource_id):\\n package_type = _get_package_type(id)\\n context = {\\n u'model': model,\\n u'session': model.Session,\\n u'user': g.user,\\n u'for_view': True,\\n u'auth_user_obj': g.userobj\\n }\\n data_dict = {u'id': id}\\n\\n try:\\n check_access(u'package_update', context, data_dict)\\n except NotAuthorized:\\n return base.abort(\\n 403,\\n _(u'User %r not authorized to edit %s') % (g.user, id)\\n )\\n # check if package exists\\n try:\\n pkg_dict = get_action(u'package_show')(context, data_dict)\\n pkg = context[u'package']\\n except (NotFound, NotAuthorized):\\n return base.abort(404, _(u'Dataset not found'))\\n\\n try:\\n resource = get_action(u'resource_show')(context, {u'id': resource_id})\\n views = get_action(u'resource_view_list')(\\n context, {\\n u'id': resource_id\\n }\\n )\\n\\n except NotFound:\\n return base.abort(404, _(u'Resource not found'))\\n except NotAuthorized:\\n return base.abort(403, _(u'Unauthorized to read resource %s') % id)\\n\\n _setup_template_variables(context, {u'id': id}, package_type=package_type)\\n\\n # TODO: remove\\n g.pkg_dict = pkg_dict\\n g.pkg = pkg\\n g.resource = resource\\n g.views = views\\n\\n return base.render(\\n u'package/resource_views.html', {\\n u'pkg_dict': pkg_dict,\\n u'pkg': pkg,\\n u'resource': resource,\\n u'views': views\\n }\\n )\\n\\n\\ndef view(package_type, id, resource_id, view_id=None):\\n \\\"\\\"\\\"\\n Embedded page for a resource view.\\n\\n Depending on the type, different views are loaded. This could be an\\n img tag where the image is loaded directly or an iframe that embeds a\\n webpage or a recline preview.\\n \\\"\\\"\\\"\\n context = {\\n u'model': model,\\n u'session': model.Session,\\n u'user': g.user,\\n u'auth_user_obj': g.userobj\\n }\\n\\n try:\\n package = get_action(u'package_show')(context, {u'id': id})\\n except (NotFound, NotAuthorized):\\n return base.abort(404, _(u'Dataset not found'))\\n\\n try:\\n resource = get_action(u'resource_show')(context, {u'id': resource_id})\\n except (NotFound, NotAuthorized):\\n return base.abort(404, _(u'Resource not found'))\\n\\n view = None\\n if request.params.get(u'resource_view', u''):\\n try:\\n view = json.loads(request.params.get(u'resource_view', u''))\\n except ValueError:\\n return base.abort(409, _(u'Bad resource view data'))\\n elif view_id:\\n try:\\n view = get_action(u'resource_view_show')(context, {u'id': view_id})\\n except (NotFound, NotAuthorized):\\n return base.abort(404, _(u'Resource view not found'))\\n\\n if not view or not isinstance(view, dict):\\n return base.abort(404, _(u'Resource view not supplied'))\\n\\n return h.rendered_resource_view(view, resource, package, embed=True)\\n\\n\\n# FIXME: could anyone think about better name?\\nclass EditResourceViewView(MethodView):\\n def _prepare(self, id, resource_id):\\n context = {\\n u'model': model,\\n u'session': model.Session,\\n u'user': g.user,\\n u'for_view': True,\\n u'auth_user_obj': g.userobj\\n }\\n\\n # update resource should tell us early if the user has privilages.\\n try:\\n check_access(u'resource_update', context, {u'id': resource_id})\\n except NotAuthorized:\\n return base.abort(\\n 403,\\n _(u'User %r not authorized to edit %s') % (g.user, id)\\n )\\n\\n # get resource and package data\\n try:\\n pkg_dict = get_action(u'package_show')(context, {u'id': id})\\n pkg = context[u'package']\\n except (NotFound, NotAuthorized):\\n return base.abort(404, _(u'Dataset not found'))\\n try:\\n resource = get_action(u'resource_show')(\\n context, {\\n u'id': resource_id\\n }\\n )\\n except (NotFound, NotAuthorized):\\n return base.abort(404, _(u'Resource not found'))\\n\\n # TODO: remove\\n g.pkg_dict = pkg_dict\\n g.pkg = pkg\\n g.resource = resource\\n\\n extra_vars = dict(\\n data={},\\n errors={},\\n error_summary={},\\n view_type=None,\\n to_preview=False,\\n pkg_dict=pkg_dict,\\n pkg=pkg,\\n resource=resource\\n )\\n return context, extra_vars\\n\\n def post(self, package_type, id, resource_id, view_id=None):\\n context, extra_vars = self._prepare(id, resource_id)\\n data = clean_dict(\\n dict_fns.unflatten(\\n tuplize_dict(\\n parse_params(request.form, ignore_keys=CACHE_PARAMETERS)\\n )\\n )\\n )\\n data.pop(u'save', None)\\n\\n to_preview = data.pop(u'preview', False)\\n if to_preview:\\n context[u'preview'] = True\\n to_delete = data.pop(u'delete', None)\\n data[u'resource_id'] = resource_id\\n data[u'view_type'] = request.args.get(u'view_type')\\n\\n try:\\n if to_delete:\\n data[u'id'] = view_id\\n get_action(u'resource_view_delete')(context, data)\\n elif view_id:\\n data[u'id'] = view_id\\n data = get_action(u'resource_view_update')(context, data)\\n else:\\n data = get_action(u'resource_view_create')(context, data)\\n except ValidationError as e:\\n # Could break preview if validation error\\n to_preview = False\\n extra_vars[u'errors'] = e.error_dict,\\n extra_vars[u'error_summary'] = e.error_summary\\n except NotAuthorized:\\n # This should never happen unless the user maliciously changed\\n # the resource_id in the url.\\n return base.abort(403, _(u'Unauthorized to edit resource'))\\n else:\\n if not to_preview:\\n return h.redirect_to(\\n u'{}_resource.views'.format(package_type),\\n id=id, resource_id=resource_id\\n )\\n extra_vars[u'data'] = data\\n extra_vars[u'to_preview'] = to_preview\\n return self.get(package_type, id, resource_id, view_id, extra_vars)\\n\\n def get(\\n self, package_type, id, resource_id, view_id=None, post_extra=None\\n ):\\n context, extra_vars = self._prepare(id, resource_id)\\n to_preview = extra_vars[u'to_preview']\\n if post_extra:\\n extra_vars.update(post_extra)\\n\\n package_type = _get_package_type(id)\\n data = extra_vars[u'data'] if u'data' in extra_vars else None\\n if data and u'view_type' in data:\\n view_type = data.get(u'view_type')\\n else:\\n view_type = request.args.get(u'view_type')\\n\\n # view_id exists only when updating\\n if view_id:\\n if not data or not view_type:\\n try:\\n view_data = get_action(u'resource_view_show')(\\n context, {\\n u'id': view_id\\n }\\n )\\n view_type = view_data[u'view_type']\\n if data:\\n data.update(view_data)\\n else:\\n data = view_data\\n except (NotFound, NotAuthorized):\\n return base.abort(404, _(u'View not found'))\\n\\n # might as well preview when loading good existing view\\n if not extra_vars[u'errors']:\\n to_preview = True\\n\\n data[u'view_type'] = view_type\\n view_plugin = lib_datapreview.get_view_plugin(view_type)\\n if not view_plugin:\\n return base.abort(404, _(u'View Type Not found'))\\n\\n _setup_template_variables(\\n context, {u'id': id}, package_type=package_type\\n )\\n\\n data_dict = {\\n u'package': extra_vars[u'pkg_dict'],\\n u'resource': extra_vars[u'resource'],\\n u'resource_view': data\\n }\\n\\n view_template = view_plugin.view_template(context, data_dict)\\n form_template = view_plugin.form_template(context, data_dict)\\n\\n extra_vars.update({\\n u'form_template': form_template,\\n u'view_template': view_template,\\n u'data': data,\\n u'to_preview': to_preview,\\n u'datastore_available': plugins.plugin_loaded(u'datastore')\\n })\\n extra_vars.update(\\n view_plugin.setup_template_variables(context, data_dict) or {}\\n )\\n extra_vars.update(data_dict)\\n\\n if view_id:\\n return base.render(u'package/edit_view.html', extra_vars)\\n\\n return base.render(u'package/new_view.html', extra_vars)\\n\\n\\ndef _parse_recline_state(params):\\n state_version = int(request.args.get(u'state_version', u'1'))\\n if state_version != 1:\\n return None\\n\\n recline_state = {}\\n for k, v in request.args.items():\\n try:\\n v = h.json.loads(v)\\n except ValueError:\\n pass\\n recline_state[k] = v\\n\\n recline_state.pop(u'width', None)\\n recline_state.pop(u'height', None)\\n recline_state[u'readOnly'] = True\\n\\n # previous versions of recline setup used elasticsearch_url attribute\\n # for data api url - see http://trac.ckan.org/ticket/2639\\n # fix by relocating this to url attribute which is the default location\\n if u'dataset' in recline_state and u'elasticsearch_url' in recline_state[\\n u'dataset'\\n ]:\\n recline_state[u'dataset'][u'url'] = recline_state[u'dataset'][\\n u'elasticsearch_url'\\n ]\\n\\n # Ensure only the currentView is available\\n # default to grid view if none specified\\n if not recline_state.get(u'currentView', None):\\n recline_state[u'currentView'] = u'grid'\\n for k in recline_state.keys():\\n if k.startswith(u'view-') and \\\\\\n not k.endswith(recline_state[u'currentView']):\\n recline_state.pop(k)\\n return recline_state\\n\\n\\ndef embedded_dataviewer(package_type, id, resource_id, width=500, height=500):\\n \\\"\\\"\\\"\\n Embedded page for a read-only resource dataview. Allows\\n for width and height to be specified as part of the\\n querystring (as well as accepting them via routes).\\n \\\"\\\"\\\"\\n context = {\\n u'model': model,\\n u'session': model.Session,\\n u'user': g.user,\\n u'auth_user_obj': g.userobj\\n }\\n\\n try:\\n resource = get_action(u'resource_show')(context, {u'id': resource_id})\\n package = get_action(u'package_show')(context, {u'id': id})\\n resource_json = h.json.dumps(resource)\\n\\n # double check that the resource belongs to the specified package\\n if not resource[u'id'] in [r[u'id'] for r in package[u'resources']]:\\n raise NotFound\\n dataset_type = package[u'type'] or package_type\\n\\n except (NotFound, NotAuthorized):\\n return base.abort(404, _(u'Resource not found'))\\n\\n # Construct the recline state\\n state_version = int(request.args.get(u'state_version', u'1'))\\n recline_state = _parse_recline_state(request.args)\\n if recline_state is None:\\n return base.abort(\\n 400, (\\n u'\\\"state\\\" parameter must be a valid recline '\\n u'state (version %d)' % state_version\\n )\\n )\\n\\n recline_state = h.json.dumps(recline_state)\\n\\n width = max(int(request.args.get(u'width', width)), 100)\\n height = max(int(request.args.get(u'height', height)), 100)\\n embedded = True\\n\\n # TODO: remove\\n g.resource = resource\\n g.package = package\\n g.resource_json = resource_json\\n g.recline_state = recline_state\\n g.width = width\\n g.height = height\\n g.embedded = embedded\\n\\n return base.render(\\n u'package/resource_embedded_dataviewer.html', {\\n u'dataset_type': dataset_type,\\n u'resource': resource,\\n u'package': package,\\n u'resource_json': resource_json,\\n u'width': width,\\n u'height': height,\\n u'embedded': embedded,\\n u'recline_state': recline_state\\n }\\n )\\n\\n\\ndef datapreview(package_type, id, resource_id):\\n \\\"\\\"\\\"\\n Embedded page for a resource data-preview.\\n\\n Depending on the type, different previews are loaded. This could be an\\n img tag where the image is loaded directly or an iframe that embeds a\\n webpage, or a recline preview.\\n \\\"\\\"\\\"\\n context = {\\n u'model': model,\\n u'session': model.Session,\\n u'user': g.user,\\n u'auth_user_obj': g.userobj\\n }\\n\\n try:\\n resource = get_action(u'resource_show')(context, {u'id': resource_id})\\n package = get_action(u'package_show')(context, {u'id': id})\\n\\n data_dict = {u'resource': resource, u'package': package}\\n\\n preview_plugin = lib_datapreview.get_preview_plugin(data_dict)\\n\\n if preview_plugin is None:\\n return base.abort(409, _(u'No preview has been defined.'))\\n\\n preview_plugin.setup_template_variables(context, data_dict)\\n resource_json = json.dumps(resource)\\n dataset_type = package[u'type'] or package_type\\n\\n # TODO: remove\\n g.resource = resource\\n g.package = package\\n g.resource_json = resource_json\\n\\n except (NotFound, NotAuthorized):\\n return base.abort(404, _(u'Resource not found'))\\n else:\\n return base.render(\\n preview_plugin.preview_template(context, data_dict), {\\n u'dataset_type': dataset_type,\\n u'resource': resource,\\n u'package': package,\\n u'resource_json': resource_json\\n }\\n )\\n\\n\\ndef register_dataset_plugin_rules(blueprint):\\n blueprint.add_url_rule(u'/new', view_func=CreateView.as_view(str(u'new')))\\n blueprint.add_url_rule(\\n u'/<resource_id>', view_func=read, strict_slashes=False)\\n blueprint.add_url_rule(\\n u'/<resource_id>/edit', view_func=EditView.as_view(str(u'edit'))\\n )\\n blueprint.add_url_rule(\\n u'/<resource_id>/delete', view_func=DeleteView.as_view(str(u'delete'))\\n )\\n\\n blueprint.add_url_rule(u'/<resource_id>/download', view_func=download)\\n blueprint.add_url_rule(u'/<resource_id>/views', view_func=views)\\n blueprint.add_url_rule(u'/<resource_id>/view', view_func=view)\\n blueprint.add_url_rule(u'/<resource_id>/view/<view_id>', view_func=view)\\n blueprint.add_url_rule(\\n u'/<resource_id>/download/<filename>', view_func=download\\n )\\n\\n _edit_view = EditResourceViewView.as_view(str(u'edit_view'))\\n blueprint.add_url_rule(u'/<resource_id>/new_view', view_func=_edit_view)\\n blueprint.add_url_rule(\\n u'/<resource_id>/edit_view/<view_id>', view_func=_edit_view\\n )\\n blueprint.add_url_rule(\\n u'/<resource_id>/embed', view_func=embedded_dataviewer)\\n blueprint.add_url_rule(\\n u'/<resource_id>/viewer',\\n view_func=embedded_dataviewer,\\n defaults={\\n u'width': u\\\"960\\\",\\n u'height': u\\\"800\\\"\\n }\\n )\\n blueprint.add_url_rule(u'/<resource_id>/preview', view_func=datapreview)\\n\\n\\nregister_dataset_plugin_rules(resource)\\nregister_dataset_plugin_rules(prefixed_resource)\\n\",\n \"path\": \"ckan/views/resource.py\"\n }\n]","isConversational":false},"pr_diff":{"kind":"string","value":"diff --git a/ckan/tests/controllers/test_package.py b/ckan/tests/controllers/test_package.py\nindex 6107a828d19..c7025ddf566 100644\n--- a/ckan/tests/controllers/test_package.py\n+++ b/ckan/tests/controllers/test_package.py\n@@ -1065,6 +1065,29 @@ def test_anonymous_users_cannot_edit_resource(self, app):\n )\n \n \n+@pytest.mark.usefixtures(\"clean_db\", \"with_plugins\", \"with_request_context\")\n+class TestResourceDownload(object):\n+\n+ def test_resource_download_content_type(self, create_with_upload, app):\n+\n+ dataset = factories.Dataset()\n+ resource = create_with_upload(\n+ u\"hello,world\", u\"file.csv\",\n+ package_id=dataset[u\"id\"]\n+ )\n+\n+ assert resource[u\"mimetype\"] == u\"text/csv\"\n+ url = url_for(\n+ u\"{}_resource.download\".format(dataset[u\"type\"]),\n+ id=dataset[u\"id\"],\n+ resource_id=resource[u\"id\"],\n+ )\n+\n+ response = app.get(url)\n+\n+ assert response.headers[u\"Content-Type\"] == u\"text/csv\"\n+\n+\n @pytest.mark.ckan_config(\"ckan.plugins\", \"image_view\")\n @pytest.mark.usefixtures(\"clean_db\", \"with_plugins\", \"with_request_context\")\n class TestResourceView(object):\ndiff --git a/ckan/views/resource.py b/ckan/views/resource.py\nindex 76e83f525fb..920ee3d8841 100644\n--- a/ckan/views/resource.py\n+++ b/ckan/views/resource.py\n@@ -172,7 +172,11 @@ def download(package_type, id, resource_id, filename=None):\n if rsc.get(u'url_type') == u'upload':\n upload = uploader.get_resource_uploader(rsc)\n filepath = upload.get_path(rsc[u'id'])\n- return flask.send_file(filepath)\n+ resp = flask.send_file(filepath)\n+ if rsc.get('mimetype'):\n+ resp.headers['Content-Type'] = rsc['mimetype']\n+ return resp\n+\n elif u'url' not in rsc:\n return base.abort(404, _(u'No download is available'))\n return h.redirect_to(rsc[u'url'])\n"}}},{"rowIdx":10597,"cells":{"in_source_id":{"kind":"string","value":"python__mypy-9462"},"issue":{"kind":"string","value":"TypeVar with Any upper bound generates missing attribute errors is used as Type[T]\nImagine the following code:\r\n\r\n```\r\nclass Foo():\r\n NAME: ClassVar[str] = \"\"\r\n\r\nT = TypeVar(\"T\", bound=Foo)\r\n\r\ndef create_foo(object_class: Type[T]) -> T:\r\n print(T.NAME) # Generates error in static analysis\r\n return T()\r\n```\r\n\r\nWhen this code is run through the static analysis, the marked line is flagged as an error because mypy thinks that `T` does not have the `NAME` attribute. When the same code is run through the dynamic analysis, the code works fine.\n"},"before_files":{"kind":"list like","value":[{"content":"\"\"\"Type checking of attribute access\"\"\"\n\nfrom typing import cast, Callable, Optional, Union, Sequence\nfrom typing_extensions import TYPE_CHECKING\n\nfrom mypy.types import (\n Type, Instance, AnyType, TupleType, TypedDictType, CallableType, FunctionLike,\n TypeVarLikeDef, Overloaded, TypeVarType, UnionType, PartialType, TypeOfAny, LiteralType,\n DeletedType, NoneType, TypeType, has_type_vars, get_proper_type, ProperType\n)\nfrom mypy.nodes import (\n TypeInfo, FuncBase, Var, FuncDef, SymbolNode, SymbolTable, Context,\n MypyFile, TypeVarExpr, ARG_POS, ARG_STAR, ARG_STAR2, Decorator,\n OverloadedFuncDef, TypeAlias, TempNode, is_final_node,\n SYMBOL_FUNCBASE_TYPES,\n)\nfrom mypy.messages import MessageBuilder\nfrom mypy.maptype import map_instance_to_supertype\nfrom mypy.expandtype import expand_type_by_instance, freshen_function_type_vars\nfrom mypy.erasetype import erase_typevars\nfrom mypy.plugin import AttributeContext\nfrom mypy.typeanal import set_any_tvars\nfrom mypy import message_registry\nfrom mypy import subtypes\nfrom mypy import meet\nfrom mypy.typeops import (\n tuple_fallback, bind_self, erase_to_bound, class_callable, type_object_type_from_function,\n make_simplified_union, function_type,\n)\n\nif TYPE_CHECKING: # import for forward declaration only\n import mypy.checker\n\nfrom mypy import state\n\n\nclass MemberContext:\n \"\"\"Information and objects needed to type check attribute access.\n\n Look at the docstring of analyze_member_access for more information.\n \"\"\"\n\n def __init__(self,\n is_lvalue: bool,\n is_super: bool,\n is_operator: bool,\n original_type: Type,\n context: Context,\n msg: MessageBuilder,\n chk: 'mypy.checker.TypeChecker',\n self_type: Optional[Type],\n module_symbol_table: Optional[SymbolTable] = None) -> None:\n self.is_lvalue = is_lvalue\n self.is_super = is_super\n self.is_operator = is_operator\n self.original_type = original_type\n self.self_type = self_type or original_type\n self.context = context # Error context\n self.msg = msg\n self.chk = chk\n self.module_symbol_table = module_symbol_table\n\n def builtin_type(self, name: str) -> Instance:\n return self.chk.named_type(name)\n\n def not_ready_callback(self, name: str, context: Context) -> None:\n self.chk.handle_cannot_determine_type(name, context)\n\n def copy_modified(self, *, messages: Optional[MessageBuilder] = None,\n self_type: Optional[Type] = None) -> 'MemberContext':\n mx = MemberContext(self.is_lvalue, self.is_super, self.is_operator,\n self.original_type, self.context, self.msg, self.chk,\n self.self_type, self.module_symbol_table)\n if messages is not None:\n mx.msg = messages\n if self_type is not None:\n mx.self_type = self_type\n return mx\n\n\ndef analyze_member_access(name: str,\n typ: Type,\n context: Context,\n is_lvalue: bool,\n is_super: bool,\n is_operator: bool,\n msg: MessageBuilder, *,\n original_type: Type,\n chk: 'mypy.checker.TypeChecker',\n override_info: Optional[TypeInfo] = None,\n in_literal_context: bool = False,\n self_type: Optional[Type] = None,\n module_symbol_table: Optional[SymbolTable] = None) -> Type:\n \"\"\"Return the type of attribute 'name' of 'typ'.\n\n The actual implementation is in '_analyze_member_access' and this docstring\n also applies to it.\n\n This is a general operation that supports various different variations:\n\n 1. lvalue or non-lvalue access (setter or getter access)\n 2. supertype access when using super() (is_super == True and\n 'override_info' should refer to the supertype)\n\n 'original_type' is the most precise inferred or declared type of the base object\n that we have available. When looking for an attribute of 'typ', we may perform\n recursive calls targeting the fallback type, and 'typ' may become some supertype\n of 'original_type'. 'original_type' is always preserved as the 'typ' type used in\n the initial, non-recursive call. The 'self_type' is a component of 'original_type'\n to which generic self should be bound (a narrower type that has a fallback to instance).\n Currently this is used only for union types.\n\n 'module_symbol_table' is passed to this function if 'typ' is actually a module\n and we want to keep track of the available attributes of the module (since they\n are not available via the type object directly)\n \"\"\"\n mx = MemberContext(is_lvalue,\n is_super,\n is_operator,\n original_type,\n context,\n msg,\n chk=chk,\n self_type=self_type,\n module_symbol_table=module_symbol_table)\n result = _analyze_member_access(name, typ, mx, override_info)\n possible_literal = get_proper_type(result)\n if (in_literal_context and isinstance(possible_literal, Instance) and\n possible_literal.last_known_value is not None):\n return possible_literal.last_known_value\n else:\n return result\n\n\ndef _analyze_member_access(name: str,\n typ: Type,\n mx: MemberContext,\n override_info: Optional[TypeInfo] = None) -> Type:\n # TODO: This and following functions share some logic with subtypes.find_member;\n # consider refactoring.\n typ = get_proper_type(typ)\n if isinstance(typ, Instance):\n return analyze_instance_member_access(name, typ, mx, override_info)\n elif isinstance(typ, AnyType):\n # The base object has dynamic type.\n return AnyType(TypeOfAny.from_another_any, source_any=typ)\n elif isinstance(typ, UnionType):\n return analyze_union_member_access(name, typ, mx)\n elif isinstance(typ, FunctionLike) and typ.is_type_obj():\n return analyze_type_callable_member_access(name, typ, mx)\n elif isinstance(typ, TypeType):\n return analyze_type_type_member_access(name, typ, mx, override_info)\n elif isinstance(typ, TupleType):\n # Actually look up from the fallback instance type.\n return _analyze_member_access(name, tuple_fallback(typ), mx, override_info)\n elif isinstance(typ, (TypedDictType, LiteralType, FunctionLike)):\n # Actually look up from the fallback instance type.\n return _analyze_member_access(name, typ.fallback, mx, override_info)\n elif isinstance(typ, NoneType):\n return analyze_none_member_access(name, typ, mx)\n elif isinstance(typ, TypeVarType):\n return _analyze_member_access(name, typ.upper_bound, mx, override_info)\n elif isinstance(typ, DeletedType):\n mx.msg.deleted_as_rvalue(typ, mx.context)\n return AnyType(TypeOfAny.from_error)\n if mx.chk.should_suppress_optional_error([typ]):\n return AnyType(TypeOfAny.from_error)\n return mx.msg.has_no_attr(mx.original_type, typ, name, mx.context, mx.module_symbol_table)\n\n\n# The several functions that follow implement analyze_member_access for various\n# types and aren't documented individually.\n\n\ndef analyze_instance_member_access(name: str,\n typ: Instance,\n mx: MemberContext,\n override_info: Optional[TypeInfo]) -> Type:\n if name == '__init__' and not mx.is_super:\n # Accessing __init__ in statically typed code would compromise\n # type safety unless used via super().\n mx.msg.fail(message_registry.CANNOT_ACCESS_INIT, mx.context)\n return AnyType(TypeOfAny.from_error)\n\n # The base object has an instance type.\n\n info = typ.type\n if override_info:\n info = override_info\n\n if (state.find_occurrences and\n info.name == state.find_occurrences[0] and\n name == state.find_occurrences[1]):\n mx.msg.note(\"Occurrence of '{}.{}'\".format(*state.find_occurrences), mx.context)\n\n # Look up the member. First look up the method dictionary.\n method = info.get_method(name)\n if method:\n if method.is_property:\n assert isinstance(method, OverloadedFuncDef)\n first_item = cast(Decorator, method.items[0])\n return analyze_var(name, first_item.var, typ, info, mx)\n if mx.is_lvalue:\n mx.msg.cant_assign_to_method(mx.context)\n signature = function_type(method, mx.builtin_type('builtins.function'))\n signature = freshen_function_type_vars(signature)\n if name == '__new__':\n # __new__ is special and behaves like a static method -- don't strip\n # the first argument.\n pass\n else:\n if isinstance(signature, FunctionLike) and name != '__call__':\n # TODO: use proper treatment of special methods on unions instead\n # of this hack here and below (i.e. mx.self_type).\n dispatched_type = meet.meet_types(mx.original_type, typ)\n signature = check_self_arg(signature, dispatched_type, method.is_class,\n mx.context, name, mx.msg)\n signature = bind_self(signature, mx.self_type, is_classmethod=method.is_class)\n typ = map_instance_to_supertype(typ, method.info)\n member_type = expand_type_by_instance(signature, typ)\n freeze_type_vars(member_type)\n return member_type\n else:\n # Not a method.\n return analyze_member_var_access(name, typ, info, mx)\n\n\ndef analyze_type_callable_member_access(name: str,\n typ: FunctionLike,\n mx: MemberContext) -> Type:\n # Class attribute.\n # TODO super?\n ret_type = typ.items()[0].ret_type\n assert isinstance(ret_type, ProperType)\n if isinstance(ret_type, TupleType):\n ret_type = tuple_fallback(ret_type)\n if isinstance(ret_type, Instance):\n if not mx.is_operator:\n # When Python sees an operator (eg `3 == 4`), it automatically translates that\n # into something like `int.__eq__(3, 4)` instead of `(3).__eq__(4)` as an\n # optimization.\n #\n # While it normally it doesn't matter which of the two versions are used, it\n # does cause inconsistencies when working with classes. For example, translating\n # `int == int` to `int.__eq__(int)` would not work since `int.__eq__` is meant to\n # compare two int _instances_. What we really want is `type(int).__eq__`, which\n # is meant to compare two types or classes.\n #\n # This check makes sure that when we encounter an operator, we skip looking up\n # the corresponding method in the current instance to avoid this edge case.\n # See https://github.com/python/mypy/pull/1787 for more info.\n # TODO: do not rely on same type variables being present in all constructor overloads.\n result = analyze_class_attribute_access(ret_type, name, mx,\n original_vars=typ.items()[0].variables)\n if result:\n return result\n # Look up from the 'type' type.\n return _analyze_member_access(name, typ.fallback, mx)\n else:\n assert False, 'Unexpected type {}'.format(repr(ret_type))\n\n\ndef analyze_type_type_member_access(name: str,\n typ: TypeType,\n mx: MemberContext,\n override_info: Optional[TypeInfo]) -> Type:\n # Similar to analyze_type_callable_attribute_access.\n item = None\n fallback = mx.builtin_type('builtins.type')\n ignore_messages = mx.msg.copy()\n ignore_messages.disable_errors()\n if isinstance(typ.item, Instance):\n item = typ.item\n elif isinstance(typ.item, AnyType):\n mx = mx.copy_modified(messages=ignore_messages)\n return _analyze_member_access(name, fallback, mx, override_info)\n elif isinstance(typ.item, TypeVarType):\n upper_bound = get_proper_type(typ.item.upper_bound)\n if isinstance(upper_bound, Instance):\n item = upper_bound\n elif isinstance(upper_bound, TupleType):\n item = tuple_fallback(upper_bound)\n elif isinstance(typ.item, TupleType):\n item = tuple_fallback(typ.item)\n elif isinstance(typ.item, FunctionLike) and typ.item.is_type_obj():\n item = typ.item.fallback\n elif isinstance(typ.item, TypeType):\n # Access member on metaclass object via Type[Type[C]]\n if isinstance(typ.item.item, Instance):\n item = typ.item.item.type.metaclass_type\n if item and not mx.is_operator:\n # See comment above for why operators are skipped\n result = analyze_class_attribute_access(item, name, mx, override_info)\n if result:\n if not (isinstance(get_proper_type(result), AnyType) and item.type.fallback_to_any):\n return result\n else:\n # We don't want errors on metaclass lookup for classes with Any fallback\n mx = mx.copy_modified(messages=ignore_messages)\n if item is not None:\n fallback = item.type.metaclass_type or fallback\n return _analyze_member_access(name, fallback, mx, override_info)\n\n\ndef analyze_union_member_access(name: str, typ: UnionType, mx: MemberContext) -> Type:\n mx.msg.disable_type_names += 1\n results = []\n for subtype in typ.relevant_items():\n # Self types should be bound to every individual item of a union.\n item_mx = mx.copy_modified(self_type=subtype)\n results.append(_analyze_member_access(name, subtype, item_mx))\n mx.msg.disable_type_names -= 1\n return make_simplified_union(results)\n\n\ndef analyze_none_member_access(name: str, typ: NoneType, mx: MemberContext) -> Type:\n if mx.chk.should_suppress_optional_error([typ]):\n return AnyType(TypeOfAny.from_error)\n is_python_3 = mx.chk.options.python_version[0] >= 3\n # In Python 2 \"None\" has exactly the same attributes as \"object\". Python 3 adds a single\n # extra attribute, \"__bool__\".\n if is_python_3 and name == '__bool__':\n return CallableType(arg_types=[],\n arg_kinds=[],\n arg_names=[],\n ret_type=mx.builtin_type('builtins.bool'),\n fallback=mx.builtin_type('builtins.function'))\n else:\n return _analyze_member_access(name, mx.builtin_type('builtins.object'), mx)\n\n\ndef analyze_member_var_access(name: str,\n itype: Instance,\n info: TypeInfo,\n mx: MemberContext) -> Type:\n \"\"\"Analyse attribute access that does not target a method.\n\n This is logically part of analyze_member_access and the arguments are similar.\n\n original_type is the type of E in the expression E.var\n \"\"\"\n # It was not a method. Try looking up a variable.\n v = lookup_member_var_or_accessor(info, name, mx.is_lvalue)\n\n vv = v\n if isinstance(vv, Decorator):\n # The associated Var node of a decorator contains the type.\n v = vv.var\n\n if isinstance(vv, TypeInfo):\n # If the associated variable is a TypeInfo synthesize a Var node for\n # the purposes of type checking. This enables us to type check things\n # like accessing class attributes on an inner class.\n v = Var(name, type=type_object_type(vv, mx.builtin_type))\n v.info = info\n\n if isinstance(vv, TypeAlias) and isinstance(get_proper_type(vv.target), Instance):\n # Similar to the above TypeInfo case, we allow using\n # qualified type aliases in runtime context if it refers to an\n # instance type. For example:\n # class C:\n # A = List[int]\n # x = C.A() <- this is OK\n typ = instance_alias_type(vv, mx.builtin_type)\n v = Var(name, type=typ)\n v.info = info\n\n if isinstance(v, Var):\n implicit = info[name].implicit\n\n # An assignment to final attribute is always an error,\n # independently of types.\n if mx.is_lvalue and not mx.chk.get_final_context():\n check_final_member(name, info, mx.msg, mx.context)\n\n return analyze_var(name, v, itype, info, mx, implicit=implicit)\n elif isinstance(v, FuncDef):\n assert False, \"Did not expect a function\"\n elif (not v and name not in ['__getattr__', '__setattr__', '__getattribute__'] and\n not mx.is_operator):\n if not mx.is_lvalue:\n for method_name in ('__getattribute__', '__getattr__'):\n method = info.get_method(method_name)\n # __getattribute__ is defined on builtins.object and returns Any, so without\n # the guard this search will always find object.__getattribute__ and conclude\n # that the attribute exists\n if method and method.info.fullname != 'builtins.object':\n function = function_type(method, mx.builtin_type('builtins.function'))\n bound_method = bind_self(function, mx.self_type)\n typ = map_instance_to_supertype(itype, method.info)\n getattr_type = get_proper_type(expand_type_by_instance(bound_method, typ))\n if isinstance(getattr_type, CallableType):\n result = getattr_type.ret_type\n\n # Call the attribute hook before returning.\n fullname = '{}.{}'.format(method.info.fullname, name)\n hook = mx.chk.plugin.get_attribute_hook(fullname)\n if hook:\n result = hook(AttributeContext(get_proper_type(mx.original_type),\n result, mx.context, mx.chk))\n return result\n else:\n setattr_meth = info.get_method('__setattr__')\n if setattr_meth and setattr_meth.info.fullname != 'builtins.object':\n setattr_func = function_type(setattr_meth, mx.builtin_type('builtins.function'))\n bound_type = bind_self(setattr_func, mx.self_type)\n typ = map_instance_to_supertype(itype, setattr_meth.info)\n setattr_type = get_proper_type(expand_type_by_instance(bound_type, typ))\n if isinstance(setattr_type, CallableType) and len(setattr_type.arg_types) > 0:\n return setattr_type.arg_types[-1]\n\n if itype.type.fallback_to_any:\n return AnyType(TypeOfAny.special_form)\n\n # Could not find the member.\n if mx.is_super:\n mx.msg.undefined_in_superclass(name, mx.context)\n return AnyType(TypeOfAny.from_error)\n else:\n if mx.chk and mx.chk.should_suppress_optional_error([itype]):\n return AnyType(TypeOfAny.from_error)\n return mx.msg.has_no_attr(\n mx.original_type, itype, name, mx.context, mx.module_symbol_table\n )\n\n\ndef check_final_member(name: str, info: TypeInfo, msg: MessageBuilder, ctx: Context) -> None:\n \"\"\"Give an error if the name being assigned was declared as final.\"\"\"\n for base in info.mro:\n sym = base.names.get(name)\n if sym and is_final_node(sym.node):\n msg.cant_assign_to_final(name, attr_assign=True, ctx=ctx)\n\n\ndef analyze_descriptor_access(instance_type: Type,\n descriptor_type: Type,\n builtin_type: Callable[[str], Instance],\n msg: MessageBuilder,\n context: Context, *,\n chk: 'mypy.checker.TypeChecker') -> Type:\n \"\"\"Type check descriptor access.\n\n Arguments:\n instance_type: The type of the instance on which the descriptor\n attribute is being accessed (the type of ``a`` in ``a.f`` when\n ``f`` is a descriptor).\n descriptor_type: The type of the descriptor attribute being accessed\n (the type of ``f`` in ``a.f`` when ``f`` is a descriptor).\n context: The node defining the context of this inference.\n Return:\n The return type of the appropriate ``__get__`` overload for the descriptor.\n \"\"\"\n instance_type = get_proper_type(instance_type)\n descriptor_type = get_proper_type(descriptor_type)\n\n if isinstance(descriptor_type, UnionType):\n # Map the access over union types\n return make_simplified_union([\n analyze_descriptor_access(instance_type, typ, builtin_type,\n msg, context, chk=chk)\n for typ in descriptor_type.items\n ])\n elif not isinstance(descriptor_type, Instance):\n return descriptor_type\n\n if not descriptor_type.type.has_readable_member('__get__'):\n return descriptor_type\n\n dunder_get = descriptor_type.type.get_method('__get__')\n\n if dunder_get is None:\n msg.fail(message_registry.DESCRIPTOR_GET_NOT_CALLABLE.format(descriptor_type), context)\n return AnyType(TypeOfAny.from_error)\n\n function = function_type(dunder_get, builtin_type('builtins.function'))\n bound_method = bind_self(function, descriptor_type)\n typ = map_instance_to_supertype(descriptor_type, dunder_get.info)\n dunder_get_type = expand_type_by_instance(bound_method, typ)\n\n if isinstance(instance_type, FunctionLike) and instance_type.is_type_obj():\n owner_type = instance_type.items()[0].ret_type\n instance_type = NoneType()\n elif isinstance(instance_type, TypeType):\n owner_type = instance_type.item\n instance_type = NoneType()\n else:\n owner_type = instance_type\n\n callable_name = chk.expr_checker.method_fullname(descriptor_type, \"__get__\")\n dunder_get_type = chk.expr_checker.transform_callee_type(\n callable_name, dunder_get_type,\n [TempNode(instance_type, context=context),\n TempNode(TypeType.make_normalized(owner_type), context=context)],\n [ARG_POS, ARG_POS], context, object_type=descriptor_type,\n )\n\n _, inferred_dunder_get_type = chk.expr_checker.check_call(\n dunder_get_type,\n [TempNode(instance_type, context=context),\n TempNode(TypeType.make_normalized(owner_type), context=context)],\n [ARG_POS, ARG_POS], context, object_type=descriptor_type,\n callable_name=callable_name)\n\n inferred_dunder_get_type = get_proper_type(inferred_dunder_get_type)\n if isinstance(inferred_dunder_get_type, AnyType):\n # check_call failed, and will have reported an error\n return inferred_dunder_get_type\n\n if not isinstance(inferred_dunder_get_type, CallableType):\n msg.fail(message_registry.DESCRIPTOR_GET_NOT_CALLABLE.format(descriptor_type), context)\n return AnyType(TypeOfAny.from_error)\n\n return inferred_dunder_get_type.ret_type\n\n\ndef instance_alias_type(alias: TypeAlias,\n builtin_type: Callable[[str], Instance]) -> Type:\n \"\"\"Type of a type alias node targeting an instance, when appears in runtime context.\n\n As usual, we first erase any unbound type variables to Any.\n \"\"\"\n target = get_proper_type(alias.target) # type: Type\n assert isinstance(get_proper_type(target),\n Instance), \"Must be called only with aliases to classes\"\n target = get_proper_type(set_any_tvars(alias, alias.line, alias.column))\n assert isinstance(target, Instance)\n tp = type_object_type(target.type, builtin_type)\n return expand_type_by_instance(tp, target)\n\n\ndef analyze_var(name: str,\n var: Var,\n itype: Instance,\n info: TypeInfo,\n mx: MemberContext, *,\n implicit: bool = False) -> Type:\n \"\"\"Analyze access to an attribute via a Var node.\n\n This is conceptually part of analyze_member_access and the arguments are similar.\n\n itype is the class object in which var is defined\n original_type is the type of E in the expression E.var\n if implicit is True, the original Var was created as an assignment to self\n \"\"\"\n # Found a member variable.\n itype = map_instance_to_supertype(itype, var.info)\n typ = var.type\n if typ:\n if isinstance(typ, PartialType):\n return mx.chk.handle_partial_var_type(typ, mx.is_lvalue, var, mx.context)\n if mx.is_lvalue and var.is_property and not var.is_settable_property:\n # TODO allow setting attributes in subclass (although it is probably an error)\n mx.msg.read_only_property(name, itype.type, mx.context)\n if mx.is_lvalue and var.is_classvar:\n mx.msg.cant_assign_to_classvar(name, mx.context)\n t = get_proper_type(expand_type_by_instance(typ, itype))\n result = t # type: Type\n typ = get_proper_type(typ)\n if var.is_initialized_in_class and isinstance(typ, FunctionLike) and not typ.is_type_obj():\n if mx.is_lvalue:\n if var.is_property:\n if not var.is_settable_property:\n mx.msg.read_only_property(name, itype.type, mx.context)\n else:\n mx.msg.cant_assign_to_method(mx.context)\n\n if not var.is_staticmethod:\n # Class-level function objects and classmethods become bound methods:\n # the former to the instance, the latter to the class.\n functype = typ\n # Use meet to narrow original_type to the dispatched type.\n # For example, assume\n # * A.f: Callable[[A1], None] where A1 <: A (maybe A1 == A)\n # * B.f: Callable[[B1], None] where B1 <: B (maybe B1 == B)\n # * x: Union[A1, B1]\n # In `x.f`, when checking `x` against A1 we assume x is compatible with A\n # and similarly for B1 when checking against B\n dispatched_type = meet.meet_types(mx.original_type, itype)\n signature = freshen_function_type_vars(functype)\n signature = check_self_arg(signature, dispatched_type, var.is_classmethod,\n mx.context, name, mx.msg)\n signature = bind_self(signature, mx.self_type, var.is_classmethod)\n expanded_signature = get_proper_type(expand_type_by_instance(signature, itype))\n freeze_type_vars(expanded_signature)\n if var.is_property:\n # A property cannot have an overloaded type => the cast is fine.\n assert isinstance(expanded_signature, CallableType)\n result = expanded_signature.ret_type\n else:\n result = expanded_signature\n else:\n if not var.is_ready:\n mx.not_ready_callback(var.name, mx.context)\n # Implicit 'Any' type.\n result = AnyType(TypeOfAny.special_form)\n fullname = '{}.{}'.format(var.info.fullname, name)\n hook = mx.chk.plugin.get_attribute_hook(fullname)\n if result and not mx.is_lvalue and not implicit:\n result = analyze_descriptor_access(mx.original_type, result, mx.builtin_type,\n mx.msg, mx.context, chk=mx.chk)\n if hook:\n result = hook(AttributeContext(get_proper_type(mx.original_type),\n result, mx.context, mx.chk))\n return result\n\n\ndef freeze_type_vars(member_type: Type) -> None:\n if not isinstance(member_type, ProperType):\n return\n if isinstance(member_type, CallableType):\n for v in member_type.variables:\n v.id.meta_level = 0\n if isinstance(member_type, Overloaded):\n for it in member_type.items():\n for v in it.variables:\n v.id.meta_level = 0\n\n\ndef lookup_member_var_or_accessor(info: TypeInfo, name: str,\n is_lvalue: bool) -> Optional[SymbolNode]:\n \"\"\"Find the attribute/accessor node that refers to a member of a type.\"\"\"\n # TODO handle lvalues\n node = info.get(name)\n if node:\n return node.node\n else:\n return None\n\n\ndef check_self_arg(functype: FunctionLike,\n dispatched_arg_type: Type,\n is_classmethod: bool,\n context: Context, name: str,\n msg: MessageBuilder) -> FunctionLike:\n \"\"\"Check that an instance has a valid type for a method with annotated 'self'.\n\n For example if the method is defined as:\n class A:\n def f(self: S) -> T: ...\n then for 'x.f' we check that meet(type(x), A) <: S. If the method is overloaded, we\n select only overloads items that satisfy this requirement. If there are no matching\n overloads, an error is generated.\n\n Note: dispatched_arg_type uses a meet to select a relevant item in case if the\n original type of 'x' is a union. This is done because several special methods\n treat union types in ad-hoc manner, so we can't use MemberContext.self_type yet.\n \"\"\"\n items = functype.items()\n if not items:\n return functype\n new_items = []\n if is_classmethod:\n dispatched_arg_type = TypeType.make_normalized(dispatched_arg_type)\n for item in items:\n if not item.arg_types or item.arg_kinds[0] not in (ARG_POS, ARG_STAR):\n # No positional first (self) argument (*args is okay).\n msg.no_formal_self(name, item, context)\n # This is pretty bad, so just return the original signature if\n # there is at least one such error.\n return functype\n else:\n selfarg = item.arg_types[0]\n if subtypes.is_subtype(dispatched_arg_type, erase_typevars(erase_to_bound(selfarg))):\n new_items.append(item)\n if not new_items:\n # Choose first item for the message (it may be not very helpful for overloads).\n msg.incompatible_self_argument(name, dispatched_arg_type, items[0],\n is_classmethod, context)\n return functype\n if len(new_items) == 1:\n return new_items[0]\n return Overloaded(new_items)\n\n\ndef analyze_class_attribute_access(itype: Instance,\n name: str,\n mx: MemberContext,\n override_info: Optional[TypeInfo] = None,\n original_vars: Optional[Sequence[TypeVarLikeDef]] = None\n ) -> Optional[Type]:\n \"\"\"Analyze access to an attribute on a class object.\n\n itype is the return type of the class object callable, original_type is the type\n of E in the expression E.var, original_vars are type variables of the class callable\n (for generic classes).\n \"\"\"\n info = itype.type\n if override_info:\n info = override_info\n\n node = info.get(name)\n if not node:\n if info.fallback_to_any:\n return AnyType(TypeOfAny.special_form)\n return None\n\n is_decorated = isinstance(node.node, Decorator)\n is_method = is_decorated or isinstance(node.node, FuncBase)\n if mx.is_lvalue:\n if is_method:\n mx.msg.cant_assign_to_method(mx.context)\n if isinstance(node.node, TypeInfo):\n mx.msg.fail(message_registry.CANNOT_ASSIGN_TO_TYPE, mx.context)\n\n # If a final attribute was declared on `self` in `__init__`, then it\n # can't be accessed on the class object.\n if node.implicit and isinstance(node.node, Var) and node.node.is_final:\n mx.msg.fail(message_registry.CANNOT_ACCESS_FINAL_INSTANCE_ATTR\n .format(node.node.name), mx.context)\n\n # An assignment to final attribute on class object is also always an error,\n # independently of types.\n if mx.is_lvalue and not mx.chk.get_final_context():\n check_final_member(name, info, mx.msg, mx.context)\n\n if info.is_enum and not (mx.is_lvalue or is_decorated or is_method):\n enum_class_attribute_type = analyze_enum_class_attribute_access(itype, name, mx)\n if enum_class_attribute_type:\n return enum_class_attribute_type\n\n t = node.type\n if t:\n if isinstance(t, PartialType):\n symnode = node.node\n assert isinstance(symnode, Var)\n return mx.chk.handle_partial_var_type(t, mx.is_lvalue, symnode, mx.context)\n\n # Find the class where method/variable was defined.\n if isinstance(node.node, Decorator):\n super_info = node.node.var.info # type: Optional[TypeInfo]\n elif isinstance(node.node, (Var, SYMBOL_FUNCBASE_TYPES)):\n super_info = node.node.info\n else:\n super_info = None\n\n # Map the type to how it would look as a defining class. For example:\n # class C(Generic[T]): ...\n # class D(C[Tuple[T, S]]): ...\n # D[int, str].method()\n # Here itype is D[int, str], isuper is C[Tuple[int, str]].\n if not super_info:\n isuper = None\n else:\n isuper = map_instance_to_supertype(itype, super_info)\n\n if isinstance(node.node, Var):\n assert isuper is not None\n # Check if original variable type has type variables. For example:\n # class C(Generic[T]):\n # x: T\n # C.x # Error, ambiguous access\n # C[int].x # Also an error, since C[int] is same as C at runtime\n if isinstance(t, TypeVarType) or has_type_vars(t):\n # Exception: access on Type[...], including first argument of class methods is OK.\n if not isinstance(get_proper_type(mx.original_type), TypeType) or node.implicit:\n if node.node.is_classvar:\n message = message_registry.GENERIC_CLASS_VAR_ACCESS\n else:\n message = message_registry.GENERIC_INSTANCE_VAR_CLASS_ACCESS\n mx.msg.fail(message, mx.context)\n\n # Erase non-mapped variables, but keep mapped ones, even if there is an error.\n # In the above example this means that we infer following types:\n # C.x -> Any\n # C[int].x -> int\n t = erase_typevars(expand_type_by_instance(t, isuper))\n\n is_classmethod = ((is_decorated and cast(Decorator, node.node).func.is_class)\n or (isinstance(node.node, FuncBase) and node.node.is_class))\n t = get_proper_type(t)\n if isinstance(t, FunctionLike) and is_classmethod:\n t = check_self_arg(t, mx.self_type, False, mx.context, name, mx.msg)\n result = add_class_tvars(t, isuper, is_classmethod,\n mx.self_type, original_vars=original_vars)\n if not mx.is_lvalue:\n result = analyze_descriptor_access(mx.original_type, result, mx.builtin_type,\n mx.msg, mx.context, chk=mx.chk)\n return result\n elif isinstance(node.node, Var):\n mx.not_ready_callback(name, mx.context)\n return AnyType(TypeOfAny.special_form)\n\n if isinstance(node.node, TypeVarExpr):\n mx.msg.fail(message_registry.CANNOT_USE_TYPEVAR_AS_EXPRESSION.format(\n info.name, name), mx.context)\n return AnyType(TypeOfAny.from_error)\n\n if isinstance(node.node, TypeInfo):\n return type_object_type(node.node, mx.builtin_type)\n\n if isinstance(node.node, MypyFile):\n # Reference to a module object.\n return mx.builtin_type('types.ModuleType')\n\n if (isinstance(node.node, TypeAlias) and\n isinstance(get_proper_type(node.node.target), Instance)):\n return instance_alias_type(node.node, mx.builtin_type)\n\n if is_decorated:\n assert isinstance(node.node, Decorator)\n if node.node.type:\n return node.node.type\n else:\n mx.not_ready_callback(name, mx.context)\n return AnyType(TypeOfAny.from_error)\n else:\n assert isinstance(node.node, FuncBase)\n typ = function_type(node.node, mx.builtin_type('builtins.function'))\n # Note: if we are accessing class method on class object, the cls argument is bound.\n # Annotated and/or explicit class methods go through other code paths above, for\n # unannotated implicit class methods we do this here.\n if node.node.is_class:\n typ = bind_self(typ, is_classmethod=True)\n return typ\n\n\ndef analyze_enum_class_attribute_access(itype: Instance,\n name: str,\n mx: MemberContext,\n ) -> Optional[Type]:\n # Skip \"_order_\" and \"__order__\", since Enum will remove it\n if name in (\"_order_\", \"__order__\"):\n return mx.msg.has_no_attr(\n mx.original_type, itype, name, mx.context, mx.module_symbol_table\n )\n # For other names surrendered by underscores, we don't make them Enum members\n if name.startswith('__') and name.endswith(\"__\") and name.replace('_', '') != '':\n return None\n\n enum_literal = LiteralType(name, fallback=itype)\n # When we analyze enums, the corresponding Instance is always considered to be erased\n # due to how the signature of Enum.__new__ is `(cls: Type[_T], value: object) -> _T`\n # in typeshed. However, this is really more of an implementation detail of how Enums\n # are typed, and we really don't want to treat every single Enum value as if it were\n # from type variable substitution. So we reset the 'erased' field here.\n return itype.copy_modified(erased=False, last_known_value=enum_literal)\n\n\ndef add_class_tvars(t: ProperType, isuper: Optional[Instance],\n is_classmethod: bool,\n original_type: Type,\n original_vars: Optional[Sequence[TypeVarLikeDef]] = None) -> Type:\n \"\"\"Instantiate type variables during analyze_class_attribute_access,\n e.g T and Q in the following:\n\n class A(Generic[T]):\n @classmethod\n def foo(cls: Type[Q]) -> Tuple[T, Q]: ...\n\n class B(A[str]): pass\n B.foo()\n\n Args:\n t: Declared type of the method (or property)\n isuper: Current instance mapped to the superclass where method was defined, this\n is usually done by map_instance_to_supertype()\n is_classmethod: True if this method is decorated with @classmethod\n original_type: The value of the type B in the expression B.foo() or the corresponding\n component in case of a union (this is used to bind the self-types)\n original_vars: Type variables of the class callable on which the method was accessed\n Returns:\n Expanded method type with added type variables (when needed).\n \"\"\"\n # TODO: verify consistency between Q and T\n\n # We add class type variables if the class method is accessed on class object\n # without applied type arguments, this matches the behavior of __init__().\n # For example (continuing the example in docstring):\n # A # The type of callable is def [T] () -> A[T], _not_ def () -> A[Any]\n # A[int] # The type of callable is def () -> A[int]\n # and\n # A.foo # The type is generic def [T] () -> Tuple[T, A[T]]\n # A[int].foo # The type is non-generic def () -> Tuple[int, A[int]]\n #\n # This behaviour is useful for defining alternative constructors for generic classes.\n # To achieve such behaviour, we add the class type variables that are still free\n # (i.e. appear in the return type of the class object on which the method was accessed).\n if isinstance(t, CallableType):\n tvars = original_vars if original_vars is not None else []\n if is_classmethod:\n t = freshen_function_type_vars(t)\n t = bind_self(t, original_type, is_classmethod=True)\n assert isuper is not None\n t = cast(CallableType, expand_type_by_instance(t, isuper))\n freeze_type_vars(t)\n return t.copy_modified(variables=list(tvars) + list(t.variables))\n elif isinstance(t, Overloaded):\n return Overloaded([cast(CallableType, add_class_tvars(item, isuper,\n is_classmethod, original_type,\n original_vars=original_vars))\n for item in t.items()])\n if isuper is not None:\n t = cast(ProperType, expand_type_by_instance(t, isuper))\n return t\n\n\ndef type_object_type(info: TypeInfo, builtin_type: Callable[[str], Instance]) -> ProperType:\n \"\"\"Return the type of a type object.\n\n For a generic type G with type variables T and S the type is generally of form\n\n Callable[..., G[T, S]]\n\n where ... are argument types for the __init__/__new__ method (without the self\n argument). Also, the fallback type will be 'type' instead of 'function'.\n \"\"\"\n\n # We take the type from whichever of __init__ and __new__ is first\n # in the MRO, preferring __init__ if there is a tie.\n init_method = info.get('__init__')\n new_method = info.get('__new__')\n if not init_method or not is_valid_constructor(init_method.node):\n # Must be an invalid class definition.\n return AnyType(TypeOfAny.from_error)\n # There *should* always be a __new__ method except the test stubs\n # lack it, so just copy init_method in that situation\n new_method = new_method or init_method\n if not is_valid_constructor(new_method.node):\n # Must be an invalid class definition.\n return AnyType(TypeOfAny.from_error)\n\n # The two is_valid_constructor() checks ensure this.\n assert isinstance(new_method.node, (SYMBOL_FUNCBASE_TYPES, Decorator))\n assert isinstance(init_method.node, (SYMBOL_FUNCBASE_TYPES, Decorator))\n\n init_index = info.mro.index(init_method.node.info)\n new_index = info.mro.index(new_method.node.info)\n\n fallback = info.metaclass_type or builtin_type('builtins.type')\n if init_index < new_index:\n method = init_method.node # type: Union[FuncBase, Decorator]\n is_new = False\n elif init_index > new_index:\n method = new_method.node\n is_new = True\n else:\n if init_method.node.info.fullname == 'builtins.object':\n # Both are defined by object. But if we've got a bogus\n # base class, we can't know for sure, so check for that.\n if info.fallback_to_any:\n # Construct a universal callable as the prototype.\n any_type = AnyType(TypeOfAny.special_form)\n sig = CallableType(arg_types=[any_type, any_type],\n arg_kinds=[ARG_STAR, ARG_STAR2],\n arg_names=[\"_args\", \"_kwds\"],\n ret_type=any_type,\n fallback=builtin_type('builtins.function'))\n return class_callable(sig, info, fallback, None, is_new=False)\n\n # Otherwise prefer __init__ in a tie. It isn't clear that this\n # is the right thing, but __new__ caused problems with\n # typeshed (#5647).\n method = init_method.node\n is_new = False\n # Construct callable type based on signature of __init__. Adjust\n # return type and insert type arguments.\n if isinstance(method, FuncBase):\n t = function_type(method, fallback)\n else:\n assert isinstance(method.type, ProperType)\n assert isinstance(method.type, FunctionLike) # is_valid_constructor() ensures this\n t = method.type\n return type_object_type_from_function(t, info, method.info, fallback, is_new)\n\n\ndef is_valid_constructor(n: Optional[SymbolNode]) -> bool:\n \"\"\"Does this node represents a valid constructor method?\n\n This includes normal functions, overloaded functions, and decorators\n that return a callable type.\n \"\"\"\n if isinstance(n, FuncBase):\n return True\n if isinstance(n, Decorator):\n return isinstance(get_proper_type(n.type), FunctionLike)\n return False\n","path":"mypy/checkmember.py"}],"string":"[\n {\n \"content\": \"\\\"\\\"\\\"Type checking of attribute access\\\"\\\"\\\"\\n\\nfrom typing import cast, Callable, Optional, Union, Sequence\\nfrom typing_extensions import TYPE_CHECKING\\n\\nfrom mypy.types import (\\n Type, Instance, AnyType, TupleType, TypedDictType, CallableType, FunctionLike,\\n TypeVarLikeDef, Overloaded, TypeVarType, UnionType, PartialType, TypeOfAny, LiteralType,\\n DeletedType, NoneType, TypeType, has_type_vars, get_proper_type, ProperType\\n)\\nfrom mypy.nodes import (\\n TypeInfo, FuncBase, Var, FuncDef, SymbolNode, SymbolTable, Context,\\n MypyFile, TypeVarExpr, ARG_POS, ARG_STAR, ARG_STAR2, Decorator,\\n OverloadedFuncDef, TypeAlias, TempNode, is_final_node,\\n SYMBOL_FUNCBASE_TYPES,\\n)\\nfrom mypy.messages import MessageBuilder\\nfrom mypy.maptype import map_instance_to_supertype\\nfrom mypy.expandtype import expand_type_by_instance, freshen_function_type_vars\\nfrom mypy.erasetype import erase_typevars\\nfrom mypy.plugin import AttributeContext\\nfrom mypy.typeanal import set_any_tvars\\nfrom mypy import message_registry\\nfrom mypy import subtypes\\nfrom mypy import meet\\nfrom mypy.typeops import (\\n tuple_fallback, bind_self, erase_to_bound, class_callable, type_object_type_from_function,\\n make_simplified_union, function_type,\\n)\\n\\nif TYPE_CHECKING: # import for forward declaration only\\n import mypy.checker\\n\\nfrom mypy import state\\n\\n\\nclass MemberContext:\\n \\\"\\\"\\\"Information and objects needed to type check attribute access.\\n\\n Look at the docstring of analyze_member_access for more information.\\n \\\"\\\"\\\"\\n\\n def __init__(self,\\n is_lvalue: bool,\\n is_super: bool,\\n is_operator: bool,\\n original_type: Type,\\n context: Context,\\n msg: MessageBuilder,\\n chk: 'mypy.checker.TypeChecker',\\n self_type: Optional[Type],\\n module_symbol_table: Optional[SymbolTable] = None) -> None:\\n self.is_lvalue = is_lvalue\\n self.is_super = is_super\\n self.is_operator = is_operator\\n self.original_type = original_type\\n self.self_type = self_type or original_type\\n self.context = context # Error context\\n self.msg = msg\\n self.chk = chk\\n self.module_symbol_table = module_symbol_table\\n\\n def builtin_type(self, name: str) -> Instance:\\n return self.chk.named_type(name)\\n\\n def not_ready_callback(self, name: str, context: Context) -> None:\\n self.chk.handle_cannot_determine_type(name, context)\\n\\n def copy_modified(self, *, messages: Optional[MessageBuilder] = None,\\n self_type: Optional[Type] = None) -> 'MemberContext':\\n mx = MemberContext(self.is_lvalue, self.is_super, self.is_operator,\\n self.original_type, self.context, self.msg, self.chk,\\n self.self_type, self.module_symbol_table)\\n if messages is not None:\\n mx.msg = messages\\n if self_type is not None:\\n mx.self_type = self_type\\n return mx\\n\\n\\ndef analyze_member_access(name: str,\\n typ: Type,\\n context: Context,\\n is_lvalue: bool,\\n is_super: bool,\\n is_operator: bool,\\n msg: MessageBuilder, *,\\n original_type: Type,\\n chk: 'mypy.checker.TypeChecker',\\n override_info: Optional[TypeInfo] = None,\\n in_literal_context: bool = False,\\n self_type: Optional[Type] = None,\\n module_symbol_table: Optional[SymbolTable] = None) -> Type:\\n \\\"\\\"\\\"Return the type of attribute 'name' of 'typ'.\\n\\n The actual implementation is in '_analyze_member_access' and this docstring\\n also applies to it.\\n\\n This is a general operation that supports various different variations:\\n\\n 1. lvalue or non-lvalue access (setter or getter access)\\n 2. supertype access when using super() (is_super == True and\\n 'override_info' should refer to the supertype)\\n\\n 'original_type' is the most precise inferred or declared type of the base object\\n that we have available. When looking for an attribute of 'typ', we may perform\\n recursive calls targeting the fallback type, and 'typ' may become some supertype\\n of 'original_type'. 'original_type' is always preserved as the 'typ' type used in\\n the initial, non-recursive call. The 'self_type' is a component of 'original_type'\\n to which generic self should be bound (a narrower type that has a fallback to instance).\\n Currently this is used only for union types.\\n\\n 'module_symbol_table' is passed to this function if 'typ' is actually a module\\n and we want to keep track of the available attributes of the module (since they\\n are not available via the type object directly)\\n \\\"\\\"\\\"\\n mx = MemberContext(is_lvalue,\\n is_super,\\n is_operator,\\n original_type,\\n context,\\n msg,\\n chk=chk,\\n self_type=self_type,\\n module_symbol_table=module_symbol_table)\\n result = _analyze_member_access(name, typ, mx, override_info)\\n possible_literal = get_proper_type(result)\\n if (in_literal_context and isinstance(possible_literal, Instance) and\\n possible_literal.last_known_value is not None):\\n return possible_literal.last_known_value\\n else:\\n return result\\n\\n\\ndef _analyze_member_access(name: str,\\n typ: Type,\\n mx: MemberContext,\\n override_info: Optional[TypeInfo] = None) -> Type:\\n # TODO: This and following functions share some logic with subtypes.find_member;\\n # consider refactoring.\\n typ = get_proper_type(typ)\\n if isinstance(typ, Instance):\\n return analyze_instance_member_access(name, typ, mx, override_info)\\n elif isinstance(typ, AnyType):\\n # The base object has dynamic type.\\n return AnyType(TypeOfAny.from_another_any, source_any=typ)\\n elif isinstance(typ, UnionType):\\n return analyze_union_member_access(name, typ, mx)\\n elif isinstance(typ, FunctionLike) and typ.is_type_obj():\\n return analyze_type_callable_member_access(name, typ, mx)\\n elif isinstance(typ, TypeType):\\n return analyze_type_type_member_access(name, typ, mx, override_info)\\n elif isinstance(typ, TupleType):\\n # Actually look up from the fallback instance type.\\n return _analyze_member_access(name, tuple_fallback(typ), mx, override_info)\\n elif isinstance(typ, (TypedDictType, LiteralType, FunctionLike)):\\n # Actually look up from the fallback instance type.\\n return _analyze_member_access(name, typ.fallback, mx, override_info)\\n elif isinstance(typ, NoneType):\\n return analyze_none_member_access(name, typ, mx)\\n elif isinstance(typ, TypeVarType):\\n return _analyze_member_access(name, typ.upper_bound, mx, override_info)\\n elif isinstance(typ, DeletedType):\\n mx.msg.deleted_as_rvalue(typ, mx.context)\\n return AnyType(TypeOfAny.from_error)\\n if mx.chk.should_suppress_optional_error([typ]):\\n return AnyType(TypeOfAny.from_error)\\n return mx.msg.has_no_attr(mx.original_type, typ, name, mx.context, mx.module_symbol_table)\\n\\n\\n# The several functions that follow implement analyze_member_access for various\\n# types and aren't documented individually.\\n\\n\\ndef analyze_instance_member_access(name: str,\\n typ: Instance,\\n mx: MemberContext,\\n override_info: Optional[TypeInfo]) -> Type:\\n if name == '__init__' and not mx.is_super:\\n # Accessing __init__ in statically typed code would compromise\\n # type safety unless used via super().\\n mx.msg.fail(message_registry.CANNOT_ACCESS_INIT, mx.context)\\n return AnyType(TypeOfAny.from_error)\\n\\n # The base object has an instance type.\\n\\n info = typ.type\\n if override_info:\\n info = override_info\\n\\n if (state.find_occurrences and\\n info.name == state.find_occurrences[0] and\\n name == state.find_occurrences[1]):\\n mx.msg.note(\\\"Occurrence of '{}.{}'\\\".format(*state.find_occurrences), mx.context)\\n\\n # Look up the member. First look up the method dictionary.\\n method = info.get_method(name)\\n if method:\\n if method.is_property:\\n assert isinstance(method, OverloadedFuncDef)\\n first_item = cast(Decorator, method.items[0])\\n return analyze_var(name, first_item.var, typ, info, mx)\\n if mx.is_lvalue:\\n mx.msg.cant_assign_to_method(mx.context)\\n signature = function_type(method, mx.builtin_type('builtins.function'))\\n signature = freshen_function_type_vars(signature)\\n if name == '__new__':\\n # __new__ is special and behaves like a static method -- don't strip\\n # the first argument.\\n pass\\n else:\\n if isinstance(signature, FunctionLike) and name != '__call__':\\n # TODO: use proper treatment of special methods on unions instead\\n # of this hack here and below (i.e. mx.self_type).\\n dispatched_type = meet.meet_types(mx.original_type, typ)\\n signature = check_self_arg(signature, dispatched_type, method.is_class,\\n mx.context, name, mx.msg)\\n signature = bind_self(signature, mx.self_type, is_classmethod=method.is_class)\\n typ = map_instance_to_supertype(typ, method.info)\\n member_type = expand_type_by_instance(signature, typ)\\n freeze_type_vars(member_type)\\n return member_type\\n else:\\n # Not a method.\\n return analyze_member_var_access(name, typ, info, mx)\\n\\n\\ndef analyze_type_callable_member_access(name: str,\\n typ: FunctionLike,\\n mx: MemberContext) -> Type:\\n # Class attribute.\\n # TODO super?\\n ret_type = typ.items()[0].ret_type\\n assert isinstance(ret_type, ProperType)\\n if isinstance(ret_type, TupleType):\\n ret_type = tuple_fallback(ret_type)\\n if isinstance(ret_type, Instance):\\n if not mx.is_operator:\\n # When Python sees an operator (eg `3 == 4`), it automatically translates that\\n # into something like `int.__eq__(3, 4)` instead of `(3).__eq__(4)` as an\\n # optimization.\\n #\\n # While it normally it doesn't matter which of the two versions are used, it\\n # does cause inconsistencies when working with classes. For example, translating\\n # `int == int` to `int.__eq__(int)` would not work since `int.__eq__` is meant to\\n # compare two int _instances_. What we really want is `type(int).__eq__`, which\\n # is meant to compare two types or classes.\\n #\\n # This check makes sure that when we encounter an operator, we skip looking up\\n # the corresponding method in the current instance to avoid this edge case.\\n # See https://github.com/python/mypy/pull/1787 for more info.\\n # TODO: do not rely on same type variables being present in all constructor overloads.\\n result = analyze_class_attribute_access(ret_type, name, mx,\\n original_vars=typ.items()[0].variables)\\n if result:\\n return result\\n # Look up from the 'type' type.\\n return _analyze_member_access(name, typ.fallback, mx)\\n else:\\n assert False, 'Unexpected type {}'.format(repr(ret_type))\\n\\n\\ndef analyze_type_type_member_access(name: str,\\n typ: TypeType,\\n mx: MemberContext,\\n override_info: Optional[TypeInfo]) -> Type:\\n # Similar to analyze_type_callable_attribute_access.\\n item = None\\n fallback = mx.builtin_type('builtins.type')\\n ignore_messages = mx.msg.copy()\\n ignore_messages.disable_errors()\\n if isinstance(typ.item, Instance):\\n item = typ.item\\n elif isinstance(typ.item, AnyType):\\n mx = mx.copy_modified(messages=ignore_messages)\\n return _analyze_member_access(name, fallback, mx, override_info)\\n elif isinstance(typ.item, TypeVarType):\\n upper_bound = get_proper_type(typ.item.upper_bound)\\n if isinstance(upper_bound, Instance):\\n item = upper_bound\\n elif isinstance(upper_bound, TupleType):\\n item = tuple_fallback(upper_bound)\\n elif isinstance(typ.item, TupleType):\\n item = tuple_fallback(typ.item)\\n elif isinstance(typ.item, FunctionLike) and typ.item.is_type_obj():\\n item = typ.item.fallback\\n elif isinstance(typ.item, TypeType):\\n # Access member on metaclass object via Type[Type[C]]\\n if isinstance(typ.item.item, Instance):\\n item = typ.item.item.type.metaclass_type\\n if item and not mx.is_operator:\\n # See comment above for why operators are skipped\\n result = analyze_class_attribute_access(item, name, mx, override_info)\\n if result:\\n if not (isinstance(get_proper_type(result), AnyType) and item.type.fallback_to_any):\\n return result\\n else:\\n # We don't want errors on metaclass lookup for classes with Any fallback\\n mx = mx.copy_modified(messages=ignore_messages)\\n if item is not None:\\n fallback = item.type.metaclass_type or fallback\\n return _analyze_member_access(name, fallback, mx, override_info)\\n\\n\\ndef analyze_union_member_access(name: str, typ: UnionType, mx: MemberContext) -> Type:\\n mx.msg.disable_type_names += 1\\n results = []\\n for subtype in typ.relevant_items():\\n # Self types should be bound to every individual item of a union.\\n item_mx = mx.copy_modified(self_type=subtype)\\n results.append(_analyze_member_access(name, subtype, item_mx))\\n mx.msg.disable_type_names -= 1\\n return make_simplified_union(results)\\n\\n\\ndef analyze_none_member_access(name: str, typ: NoneType, mx: MemberContext) -> Type:\\n if mx.chk.should_suppress_optional_error([typ]):\\n return AnyType(TypeOfAny.from_error)\\n is_python_3 = mx.chk.options.python_version[0] >= 3\\n # In Python 2 \\\"None\\\" has exactly the same attributes as \\\"object\\\". Python 3 adds a single\\n # extra attribute, \\\"__bool__\\\".\\n if is_python_3 and name == '__bool__':\\n return CallableType(arg_types=[],\\n arg_kinds=[],\\n arg_names=[],\\n ret_type=mx.builtin_type('builtins.bool'),\\n fallback=mx.builtin_type('builtins.function'))\\n else:\\n return _analyze_member_access(name, mx.builtin_type('builtins.object'), mx)\\n\\n\\ndef analyze_member_var_access(name: str,\\n itype: Instance,\\n info: TypeInfo,\\n mx: MemberContext) -> Type:\\n \\\"\\\"\\\"Analyse attribute access that does not target a method.\\n\\n This is logically part of analyze_member_access and the arguments are similar.\\n\\n original_type is the type of E in the expression E.var\\n \\\"\\\"\\\"\\n # It was not a method. Try looking up a variable.\\n v = lookup_member_var_or_accessor(info, name, mx.is_lvalue)\\n\\n vv = v\\n if isinstance(vv, Decorator):\\n # The associated Var node of a decorator contains the type.\\n v = vv.var\\n\\n if isinstance(vv, TypeInfo):\\n # If the associated variable is a TypeInfo synthesize a Var node for\\n # the purposes of type checking. This enables us to type check things\\n # like accessing class attributes on an inner class.\\n v = Var(name, type=type_object_type(vv, mx.builtin_type))\\n v.info = info\\n\\n if isinstance(vv, TypeAlias) and isinstance(get_proper_type(vv.target), Instance):\\n # Similar to the above TypeInfo case, we allow using\\n # qualified type aliases in runtime context if it refers to an\\n # instance type. For example:\\n # class C:\\n # A = List[int]\\n # x = C.A() <- this is OK\\n typ = instance_alias_type(vv, mx.builtin_type)\\n v = Var(name, type=typ)\\n v.info = info\\n\\n if isinstance(v, Var):\\n implicit = info[name].implicit\\n\\n # An assignment to final attribute is always an error,\\n # independently of types.\\n if mx.is_lvalue and not mx.chk.get_final_context():\\n check_final_member(name, info, mx.msg, mx.context)\\n\\n return analyze_var(name, v, itype, info, mx, implicit=implicit)\\n elif isinstance(v, FuncDef):\\n assert False, \\\"Did not expect a function\\\"\\n elif (not v and name not in ['__getattr__', '__setattr__', '__getattribute__'] and\\n not mx.is_operator):\\n if not mx.is_lvalue:\\n for method_name in ('__getattribute__', '__getattr__'):\\n method = info.get_method(method_name)\\n # __getattribute__ is defined on builtins.object and returns Any, so without\\n # the guard this search will always find object.__getattribute__ and conclude\\n # that the attribute exists\\n if method and method.info.fullname != 'builtins.object':\\n function = function_type(method, mx.builtin_type('builtins.function'))\\n bound_method = bind_self(function, mx.self_type)\\n typ = map_instance_to_supertype(itype, method.info)\\n getattr_type = get_proper_type(expand_type_by_instance(bound_method, typ))\\n if isinstance(getattr_type, CallableType):\\n result = getattr_type.ret_type\\n\\n # Call the attribute hook before returning.\\n fullname = '{}.{}'.format(method.info.fullname, name)\\n hook = mx.chk.plugin.get_attribute_hook(fullname)\\n if hook:\\n result = hook(AttributeContext(get_proper_type(mx.original_type),\\n result, mx.context, mx.chk))\\n return result\\n else:\\n setattr_meth = info.get_method('__setattr__')\\n if setattr_meth and setattr_meth.info.fullname != 'builtins.object':\\n setattr_func = function_type(setattr_meth, mx.builtin_type('builtins.function'))\\n bound_type = bind_self(setattr_func, mx.self_type)\\n typ = map_instance_to_supertype(itype, setattr_meth.info)\\n setattr_type = get_proper_type(expand_type_by_instance(bound_type, typ))\\n if isinstance(setattr_type, CallableType) and len(setattr_type.arg_types) > 0:\\n return setattr_type.arg_types[-1]\\n\\n if itype.type.fallback_to_any:\\n return AnyType(TypeOfAny.special_form)\\n\\n # Could not find the member.\\n if mx.is_super:\\n mx.msg.undefined_in_superclass(name, mx.context)\\n return AnyType(TypeOfAny.from_error)\\n else:\\n if mx.chk and mx.chk.should_suppress_optional_error([itype]):\\n return AnyType(TypeOfAny.from_error)\\n return mx.msg.has_no_attr(\\n mx.original_type, itype, name, mx.context, mx.module_symbol_table\\n )\\n\\n\\ndef check_final_member(name: str, info: TypeInfo, msg: MessageBuilder, ctx: Context) -> None:\\n \\\"\\\"\\\"Give an error if the name being assigned was declared as final.\\\"\\\"\\\"\\n for base in info.mro:\\n sym = base.names.get(name)\\n if sym and is_final_node(sym.node):\\n msg.cant_assign_to_final(name, attr_assign=True, ctx=ctx)\\n\\n\\ndef analyze_descriptor_access(instance_type: Type,\\n descriptor_type: Type,\\n builtin_type: Callable[[str], Instance],\\n msg: MessageBuilder,\\n context: Context, *,\\n chk: 'mypy.checker.TypeChecker') -> Type:\\n \\\"\\\"\\\"Type check descriptor access.\\n\\n Arguments:\\n instance_type: The type of the instance on which the descriptor\\n attribute is being accessed (the type of ``a`` in ``a.f`` when\\n ``f`` is a descriptor).\\n descriptor_type: The type of the descriptor attribute being accessed\\n (the type of ``f`` in ``a.f`` when ``f`` is a descriptor).\\n context: The node defining the context of this inference.\\n Return:\\n The return type of the appropriate ``__get__`` overload for the descriptor.\\n \\\"\\\"\\\"\\n instance_type = get_proper_type(instance_type)\\n descriptor_type = get_proper_type(descriptor_type)\\n\\n if isinstance(descriptor_type, UnionType):\\n # Map the access over union types\\n return make_simplified_union([\\n analyze_descriptor_access(instance_type, typ, builtin_type,\\n msg, context, chk=chk)\\n for typ in descriptor_type.items\\n ])\\n elif not isinstance(descriptor_type, Instance):\\n return descriptor_type\\n\\n if not descriptor_type.type.has_readable_member('__get__'):\\n return descriptor_type\\n\\n dunder_get = descriptor_type.type.get_method('__get__')\\n\\n if dunder_get is None:\\n msg.fail(message_registry.DESCRIPTOR_GET_NOT_CALLABLE.format(descriptor_type), context)\\n return AnyType(TypeOfAny.from_error)\\n\\n function = function_type(dunder_get, builtin_type('builtins.function'))\\n bound_method = bind_self(function, descriptor_type)\\n typ = map_instance_to_supertype(descriptor_type, dunder_get.info)\\n dunder_get_type = expand_type_by_instance(bound_method, typ)\\n\\n if isinstance(instance_type, FunctionLike) and instance_type.is_type_obj():\\n owner_type = instance_type.items()[0].ret_type\\n instance_type = NoneType()\\n elif isinstance(instance_type, TypeType):\\n owner_type = instance_type.item\\n instance_type = NoneType()\\n else:\\n owner_type = instance_type\\n\\n callable_name = chk.expr_checker.method_fullname(descriptor_type, \\\"__get__\\\")\\n dunder_get_type = chk.expr_checker.transform_callee_type(\\n callable_name, dunder_get_type,\\n [TempNode(instance_type, context=context),\\n TempNode(TypeType.make_normalized(owner_type), context=context)],\\n [ARG_POS, ARG_POS], context, object_type=descriptor_type,\\n )\\n\\n _, inferred_dunder_get_type = chk.expr_checker.check_call(\\n dunder_get_type,\\n [TempNode(instance_type, context=context),\\n TempNode(TypeType.make_normalized(owner_type), context=context)],\\n [ARG_POS, ARG_POS], context, object_type=descriptor_type,\\n callable_name=callable_name)\\n\\n inferred_dunder_get_type = get_proper_type(inferred_dunder_get_type)\\n if isinstance(inferred_dunder_get_type, AnyType):\\n # check_call failed, and will have reported an error\\n return inferred_dunder_get_type\\n\\n if not isinstance(inferred_dunder_get_type, CallableType):\\n msg.fail(message_registry.DESCRIPTOR_GET_NOT_CALLABLE.format(descriptor_type), context)\\n return AnyType(TypeOfAny.from_error)\\n\\n return inferred_dunder_get_type.ret_type\\n\\n\\ndef instance_alias_type(alias: TypeAlias,\\n builtin_type: Callable[[str], Instance]) -> Type:\\n \\\"\\\"\\\"Type of a type alias node targeting an instance, when appears in runtime context.\\n\\n As usual, we first erase any unbound type variables to Any.\\n \\\"\\\"\\\"\\n target = get_proper_type(alias.target) # type: Type\\n assert isinstance(get_proper_type(target),\\n Instance), \\\"Must be called only with aliases to classes\\\"\\n target = get_proper_type(set_any_tvars(alias, alias.line, alias.column))\\n assert isinstance(target, Instance)\\n tp = type_object_type(target.type, builtin_type)\\n return expand_type_by_instance(tp, target)\\n\\n\\ndef analyze_var(name: str,\\n var: Var,\\n itype: Instance,\\n info: TypeInfo,\\n mx: MemberContext, *,\\n implicit: bool = False) -> Type:\\n \\\"\\\"\\\"Analyze access to an attribute via a Var node.\\n\\n This is conceptually part of analyze_member_access and the arguments are similar.\\n\\n itype is the class object in which var is defined\\n original_type is the type of E in the expression E.var\\n if implicit is True, the original Var was created as an assignment to self\\n \\\"\\\"\\\"\\n # Found a member variable.\\n itype = map_instance_to_supertype(itype, var.info)\\n typ = var.type\\n if typ:\\n if isinstance(typ, PartialType):\\n return mx.chk.handle_partial_var_type(typ, mx.is_lvalue, var, mx.context)\\n if mx.is_lvalue and var.is_property and not var.is_settable_property:\\n # TODO allow setting attributes in subclass (although it is probably an error)\\n mx.msg.read_only_property(name, itype.type, mx.context)\\n if mx.is_lvalue and var.is_classvar:\\n mx.msg.cant_assign_to_classvar(name, mx.context)\\n t = get_proper_type(expand_type_by_instance(typ, itype))\\n result = t # type: Type\\n typ = get_proper_type(typ)\\n if var.is_initialized_in_class and isinstance(typ, FunctionLike) and not typ.is_type_obj():\\n if mx.is_lvalue:\\n if var.is_property:\\n if not var.is_settable_property:\\n mx.msg.read_only_property(name, itype.type, mx.context)\\n else:\\n mx.msg.cant_assign_to_method(mx.context)\\n\\n if not var.is_staticmethod:\\n # Class-level function objects and classmethods become bound methods:\\n # the former to the instance, the latter to the class.\\n functype = typ\\n # Use meet to narrow original_type to the dispatched type.\\n # For example, assume\\n # * A.f: Callable[[A1], None] where A1 <: A (maybe A1 == A)\\n # * B.f: Callable[[B1], None] where B1 <: B (maybe B1 == B)\\n # * x: Union[A1, B1]\\n # In `x.f`, when checking `x` against A1 we assume x is compatible with A\\n # and similarly for B1 when checking against B\\n dispatched_type = meet.meet_types(mx.original_type, itype)\\n signature = freshen_function_type_vars(functype)\\n signature = check_self_arg(signature, dispatched_type, var.is_classmethod,\\n mx.context, name, mx.msg)\\n signature = bind_self(signature, mx.self_type, var.is_classmethod)\\n expanded_signature = get_proper_type(expand_type_by_instance(signature, itype))\\n freeze_type_vars(expanded_signature)\\n if var.is_property:\\n # A property cannot have an overloaded type => the cast is fine.\\n assert isinstance(expanded_signature, CallableType)\\n result = expanded_signature.ret_type\\n else:\\n result = expanded_signature\\n else:\\n if not var.is_ready:\\n mx.not_ready_callback(var.name, mx.context)\\n # Implicit 'Any' type.\\n result = AnyType(TypeOfAny.special_form)\\n fullname = '{}.{}'.format(var.info.fullname, name)\\n hook = mx.chk.plugin.get_attribute_hook(fullname)\\n if result and not mx.is_lvalue and not implicit:\\n result = analyze_descriptor_access(mx.original_type, result, mx.builtin_type,\\n mx.msg, mx.context, chk=mx.chk)\\n if hook:\\n result = hook(AttributeContext(get_proper_type(mx.original_type),\\n result, mx.context, mx.chk))\\n return result\\n\\n\\ndef freeze_type_vars(member_type: Type) -> None:\\n if not isinstance(member_type, ProperType):\\n return\\n if isinstance(member_type, CallableType):\\n for v in member_type.variables:\\n v.id.meta_level = 0\\n if isinstance(member_type, Overloaded):\\n for it in member_type.items():\\n for v in it.variables:\\n v.id.meta_level = 0\\n\\n\\ndef lookup_member_var_or_accessor(info: TypeInfo, name: str,\\n is_lvalue: bool) -> Optional[SymbolNode]:\\n \\\"\\\"\\\"Find the attribute/accessor node that refers to a member of a type.\\\"\\\"\\\"\\n # TODO handle lvalues\\n node = info.get(name)\\n if node:\\n return node.node\\n else:\\n return None\\n\\n\\ndef check_self_arg(functype: FunctionLike,\\n dispatched_arg_type: Type,\\n is_classmethod: bool,\\n context: Context, name: str,\\n msg: MessageBuilder) -> FunctionLike:\\n \\\"\\\"\\\"Check that an instance has a valid type for a method with annotated 'self'.\\n\\n For example if the method is defined as:\\n class A:\\n def f(self: S) -> T: ...\\n then for 'x.f' we check that meet(type(x), A) <: S. If the method is overloaded, we\\n select only overloads items that satisfy this requirement. If there are no matching\\n overloads, an error is generated.\\n\\n Note: dispatched_arg_type uses a meet to select a relevant item in case if the\\n original type of 'x' is a union. This is done because several special methods\\n treat union types in ad-hoc manner, so we can't use MemberContext.self_type yet.\\n \\\"\\\"\\\"\\n items = functype.items()\\n if not items:\\n return functype\\n new_items = []\\n if is_classmethod:\\n dispatched_arg_type = TypeType.make_normalized(dispatched_arg_type)\\n for item in items:\\n if not item.arg_types or item.arg_kinds[0] not in (ARG_POS, ARG_STAR):\\n # No positional first (self) argument (*args is okay).\\n msg.no_formal_self(name, item, context)\\n # This is pretty bad, so just return the original signature if\\n # there is at least one such error.\\n return functype\\n else:\\n selfarg = item.arg_types[0]\\n if subtypes.is_subtype(dispatched_arg_type, erase_typevars(erase_to_bound(selfarg))):\\n new_items.append(item)\\n if not new_items:\\n # Choose first item for the message (it may be not very helpful for overloads).\\n msg.incompatible_self_argument(name, dispatched_arg_type, items[0],\\n is_classmethod, context)\\n return functype\\n if len(new_items) == 1:\\n return new_items[0]\\n return Overloaded(new_items)\\n\\n\\ndef analyze_class_attribute_access(itype: Instance,\\n name: str,\\n mx: MemberContext,\\n override_info: Optional[TypeInfo] = None,\\n original_vars: Optional[Sequence[TypeVarLikeDef]] = None\\n ) -> Optional[Type]:\\n \\\"\\\"\\\"Analyze access to an attribute on a class object.\\n\\n itype is the return type of the class object callable, original_type is the type\\n of E in the expression E.var, original_vars are type variables of the class callable\\n (for generic classes).\\n \\\"\\\"\\\"\\n info = itype.type\\n if override_info:\\n info = override_info\\n\\n node = info.get(name)\\n if not node:\\n if info.fallback_to_any:\\n return AnyType(TypeOfAny.special_form)\\n return None\\n\\n is_decorated = isinstance(node.node, Decorator)\\n is_method = is_decorated or isinstance(node.node, FuncBase)\\n if mx.is_lvalue:\\n if is_method:\\n mx.msg.cant_assign_to_method(mx.context)\\n if isinstance(node.node, TypeInfo):\\n mx.msg.fail(message_registry.CANNOT_ASSIGN_TO_TYPE, mx.context)\\n\\n # If a final attribute was declared on `self` in `__init__`, then it\\n # can't be accessed on the class object.\\n if node.implicit and isinstance(node.node, Var) and node.node.is_final:\\n mx.msg.fail(message_registry.CANNOT_ACCESS_FINAL_INSTANCE_ATTR\\n .format(node.node.name), mx.context)\\n\\n # An assignment to final attribute on class object is also always an error,\\n # independently of types.\\n if mx.is_lvalue and not mx.chk.get_final_context():\\n check_final_member(name, info, mx.msg, mx.context)\\n\\n if info.is_enum and not (mx.is_lvalue or is_decorated or is_method):\\n enum_class_attribute_type = analyze_enum_class_attribute_access(itype, name, mx)\\n if enum_class_attribute_type:\\n return enum_class_attribute_type\\n\\n t = node.type\\n if t:\\n if isinstance(t, PartialType):\\n symnode = node.node\\n assert isinstance(symnode, Var)\\n return mx.chk.handle_partial_var_type(t, mx.is_lvalue, symnode, mx.context)\\n\\n # Find the class where method/variable was defined.\\n if isinstance(node.node, Decorator):\\n super_info = node.node.var.info # type: Optional[TypeInfo]\\n elif isinstance(node.node, (Var, SYMBOL_FUNCBASE_TYPES)):\\n super_info = node.node.info\\n else:\\n super_info = None\\n\\n # Map the type to how it would look as a defining class. For example:\\n # class C(Generic[T]): ...\\n # class D(C[Tuple[T, S]]): ...\\n # D[int, str].method()\\n # Here itype is D[int, str], isuper is C[Tuple[int, str]].\\n if not super_info:\\n isuper = None\\n else:\\n isuper = map_instance_to_supertype(itype, super_info)\\n\\n if isinstance(node.node, Var):\\n assert isuper is not None\\n # Check if original variable type has type variables. For example:\\n # class C(Generic[T]):\\n # x: T\\n # C.x # Error, ambiguous access\\n # C[int].x # Also an error, since C[int] is same as C at runtime\\n if isinstance(t, TypeVarType) or has_type_vars(t):\\n # Exception: access on Type[...], including first argument of class methods is OK.\\n if not isinstance(get_proper_type(mx.original_type), TypeType) or node.implicit:\\n if node.node.is_classvar:\\n message = message_registry.GENERIC_CLASS_VAR_ACCESS\\n else:\\n message = message_registry.GENERIC_INSTANCE_VAR_CLASS_ACCESS\\n mx.msg.fail(message, mx.context)\\n\\n # Erase non-mapped variables, but keep mapped ones, even if there is an error.\\n # In the above example this means that we infer following types:\\n # C.x -> Any\\n # C[int].x -> int\\n t = erase_typevars(expand_type_by_instance(t, isuper))\\n\\n is_classmethod = ((is_decorated and cast(Decorator, node.node).func.is_class)\\n or (isinstance(node.node, FuncBase) and node.node.is_class))\\n t = get_proper_type(t)\\n if isinstance(t, FunctionLike) and is_classmethod:\\n t = check_self_arg(t, mx.self_type, False, mx.context, name, mx.msg)\\n result = add_class_tvars(t, isuper, is_classmethod,\\n mx.self_type, original_vars=original_vars)\\n if not mx.is_lvalue:\\n result = analyze_descriptor_access(mx.original_type, result, mx.builtin_type,\\n mx.msg, mx.context, chk=mx.chk)\\n return result\\n elif isinstance(node.node, Var):\\n mx.not_ready_callback(name, mx.context)\\n return AnyType(TypeOfAny.special_form)\\n\\n if isinstance(node.node, TypeVarExpr):\\n mx.msg.fail(message_registry.CANNOT_USE_TYPEVAR_AS_EXPRESSION.format(\\n info.name, name), mx.context)\\n return AnyType(TypeOfAny.from_error)\\n\\n if isinstance(node.node, TypeInfo):\\n return type_object_type(node.node, mx.builtin_type)\\n\\n if isinstance(node.node, MypyFile):\\n # Reference to a module object.\\n return mx.builtin_type('types.ModuleType')\\n\\n if (isinstance(node.node, TypeAlias) and\\n isinstance(get_proper_type(node.node.target), Instance)):\\n return instance_alias_type(node.node, mx.builtin_type)\\n\\n if is_decorated:\\n assert isinstance(node.node, Decorator)\\n if node.node.type:\\n return node.node.type\\n else:\\n mx.not_ready_callback(name, mx.context)\\n return AnyType(TypeOfAny.from_error)\\n else:\\n assert isinstance(node.node, FuncBase)\\n typ = function_type(node.node, mx.builtin_type('builtins.function'))\\n # Note: if we are accessing class method on class object, the cls argument is bound.\\n # Annotated and/or explicit class methods go through other code paths above, for\\n # unannotated implicit class methods we do this here.\\n if node.node.is_class:\\n typ = bind_self(typ, is_classmethod=True)\\n return typ\\n\\n\\ndef analyze_enum_class_attribute_access(itype: Instance,\\n name: str,\\n mx: MemberContext,\\n ) -> Optional[Type]:\\n # Skip \\\"_order_\\\" and \\\"__order__\\\", since Enum will remove it\\n if name in (\\\"_order_\\\", \\\"__order__\\\"):\\n return mx.msg.has_no_attr(\\n mx.original_type, itype, name, mx.context, mx.module_symbol_table\\n )\\n # For other names surrendered by underscores, we don't make them Enum members\\n if name.startswith('__') and name.endswith(\\\"__\\\") and name.replace('_', '') != '':\\n return None\\n\\n enum_literal = LiteralType(name, fallback=itype)\\n # When we analyze enums, the corresponding Instance is always considered to be erased\\n # due to how the signature of Enum.__new__ is `(cls: Type[_T], value: object) -> _T`\\n # in typeshed. However, this is really more of an implementation detail of how Enums\\n # are typed, and we really don't want to treat every single Enum value as if it were\\n # from type variable substitution. So we reset the 'erased' field here.\\n return itype.copy_modified(erased=False, last_known_value=enum_literal)\\n\\n\\ndef add_class_tvars(t: ProperType, isuper: Optional[Instance],\\n is_classmethod: bool,\\n original_type: Type,\\n original_vars: Optional[Sequence[TypeVarLikeDef]] = None) -> Type:\\n \\\"\\\"\\\"Instantiate type variables during analyze_class_attribute_access,\\n e.g T and Q in the following:\\n\\n class A(Generic[T]):\\n @classmethod\\n def foo(cls: Type[Q]) -> Tuple[T, Q]: ...\\n\\n class B(A[str]): pass\\n B.foo()\\n\\n Args:\\n t: Declared type of the method (or property)\\n isuper: Current instance mapped to the superclass where method was defined, this\\n is usually done by map_instance_to_supertype()\\n is_classmethod: True if this method is decorated with @classmethod\\n original_type: The value of the type B in the expression B.foo() or the corresponding\\n component in case of a union (this is used to bind the self-types)\\n original_vars: Type variables of the class callable on which the method was accessed\\n Returns:\\n Expanded method type with added type variables (when needed).\\n \\\"\\\"\\\"\\n # TODO: verify consistency between Q and T\\n\\n # We add class type variables if the class method is accessed on class object\\n # without applied type arguments, this matches the behavior of __init__().\\n # For example (continuing the example in docstring):\\n # A # The type of callable is def [T] () -> A[T], _not_ def () -> A[Any]\\n # A[int] # The type of callable is def () -> A[int]\\n # and\\n # A.foo # The type is generic def [T] () -> Tuple[T, A[T]]\\n # A[int].foo # The type is non-generic def () -> Tuple[int, A[int]]\\n #\\n # This behaviour is useful for defining alternative constructors for generic classes.\\n # To achieve such behaviour, we add the class type variables that are still free\\n # (i.e. appear in the return type of the class object on which the method was accessed).\\n if isinstance(t, CallableType):\\n tvars = original_vars if original_vars is not None else []\\n if is_classmethod:\\n t = freshen_function_type_vars(t)\\n t = bind_self(t, original_type, is_classmethod=True)\\n assert isuper is not None\\n t = cast(CallableType, expand_type_by_instance(t, isuper))\\n freeze_type_vars(t)\\n return t.copy_modified(variables=list(tvars) + list(t.variables))\\n elif isinstance(t, Overloaded):\\n return Overloaded([cast(CallableType, add_class_tvars(item, isuper,\\n is_classmethod, original_type,\\n original_vars=original_vars))\\n for item in t.items()])\\n if isuper is not None:\\n t = cast(ProperType, expand_type_by_instance(t, isuper))\\n return t\\n\\n\\ndef type_object_type(info: TypeInfo, builtin_type: Callable[[str], Instance]) -> ProperType:\\n \\\"\\\"\\\"Return the type of a type object.\\n\\n For a generic type G with type variables T and S the type is generally of form\\n\\n Callable[..., G[T, S]]\\n\\n where ... are argument types for the __init__/__new__ method (without the self\\n argument). Also, the fallback type will be 'type' instead of 'function'.\\n \\\"\\\"\\\"\\n\\n # We take the type from whichever of __init__ and __new__ is first\\n # in the MRO, preferring __init__ if there is a tie.\\n init_method = info.get('__init__')\\n new_method = info.get('__new__')\\n if not init_method or not is_valid_constructor(init_method.node):\\n # Must be an invalid class definition.\\n return AnyType(TypeOfAny.from_error)\\n # There *should* always be a __new__ method except the test stubs\\n # lack it, so just copy init_method in that situation\\n new_method = new_method or init_method\\n if not is_valid_constructor(new_method.node):\\n # Must be an invalid class definition.\\n return AnyType(TypeOfAny.from_error)\\n\\n # The two is_valid_constructor() checks ensure this.\\n assert isinstance(new_method.node, (SYMBOL_FUNCBASE_TYPES, Decorator))\\n assert isinstance(init_method.node, (SYMBOL_FUNCBASE_TYPES, Decorator))\\n\\n init_index = info.mro.index(init_method.node.info)\\n new_index = info.mro.index(new_method.node.info)\\n\\n fallback = info.metaclass_type or builtin_type('builtins.type')\\n if init_index < new_index:\\n method = init_method.node # type: Union[FuncBase, Decorator]\\n is_new = False\\n elif init_index > new_index:\\n method = new_method.node\\n is_new = True\\n else:\\n if init_method.node.info.fullname == 'builtins.object':\\n # Both are defined by object. But if we've got a bogus\\n # base class, we can't know for sure, so check for that.\\n if info.fallback_to_any:\\n # Construct a universal callable as the prototype.\\n any_type = AnyType(TypeOfAny.special_form)\\n sig = CallableType(arg_types=[any_type, any_type],\\n arg_kinds=[ARG_STAR, ARG_STAR2],\\n arg_names=[\\\"_args\\\", \\\"_kwds\\\"],\\n ret_type=any_type,\\n fallback=builtin_type('builtins.function'))\\n return class_callable(sig, info, fallback, None, is_new=False)\\n\\n # Otherwise prefer __init__ in a tie. It isn't clear that this\\n # is the right thing, but __new__ caused problems with\\n # typeshed (#5647).\\n method = init_method.node\\n is_new = False\\n # Construct callable type based on signature of __init__. Adjust\\n # return type and insert type arguments.\\n if isinstance(method, FuncBase):\\n t = function_type(method, fallback)\\n else:\\n assert isinstance(method.type, ProperType)\\n assert isinstance(method.type, FunctionLike) # is_valid_constructor() ensures this\\n t = method.type\\n return type_object_type_from_function(t, info, method.info, fallback, is_new)\\n\\n\\ndef is_valid_constructor(n: Optional[SymbolNode]) -> bool:\\n \\\"\\\"\\\"Does this node represents a valid constructor method?\\n\\n This includes normal functions, overloaded functions, and decorators\\n that return a callable type.\\n \\\"\\\"\\\"\\n if isinstance(n, FuncBase):\\n return True\\n if isinstance(n, Decorator):\\n return isinstance(get_proper_type(n.type), FunctionLike)\\n return False\\n\",\n \"path\": \"mypy/checkmember.py\"\n }\n]","isConversational":false},"after_files":{"kind":"list like","value":[{"content":"\"\"\"Type checking of attribute access\"\"\"\n\nfrom typing import cast, Callable, Optional, Union, Sequence\nfrom typing_extensions import TYPE_CHECKING\n\nfrom mypy.types import (\n Type, Instance, AnyType, TupleType, TypedDictType, CallableType, FunctionLike,\n TypeVarLikeDef, Overloaded, TypeVarType, UnionType, PartialType, TypeOfAny, LiteralType,\n DeletedType, NoneType, TypeType, has_type_vars, get_proper_type, ProperType\n)\nfrom mypy.nodes import (\n TypeInfo, FuncBase, Var, FuncDef, SymbolNode, SymbolTable, Context,\n MypyFile, TypeVarExpr, ARG_POS, ARG_STAR, ARG_STAR2, Decorator,\n OverloadedFuncDef, TypeAlias, TempNode, is_final_node,\n SYMBOL_FUNCBASE_TYPES,\n)\nfrom mypy.messages import MessageBuilder\nfrom mypy.maptype import map_instance_to_supertype\nfrom mypy.expandtype import expand_type_by_instance, freshen_function_type_vars\nfrom mypy.erasetype import erase_typevars\nfrom mypy.plugin import AttributeContext\nfrom mypy.typeanal import set_any_tvars\nfrom mypy import message_registry\nfrom mypy import subtypes\nfrom mypy import meet\nfrom mypy.typeops import (\n tuple_fallback, bind_self, erase_to_bound, class_callable, type_object_type_from_function,\n make_simplified_union, function_type,\n)\n\nif TYPE_CHECKING: # import for forward declaration only\n import mypy.checker\n\nfrom mypy import state\n\n\nclass MemberContext:\n \"\"\"Information and objects needed to type check attribute access.\n\n Look at the docstring of analyze_member_access for more information.\n \"\"\"\n\n def __init__(self,\n is_lvalue: bool,\n is_super: bool,\n is_operator: bool,\n original_type: Type,\n context: Context,\n msg: MessageBuilder,\n chk: 'mypy.checker.TypeChecker',\n self_type: Optional[Type],\n module_symbol_table: Optional[SymbolTable] = None) -> None:\n self.is_lvalue = is_lvalue\n self.is_super = is_super\n self.is_operator = is_operator\n self.original_type = original_type\n self.self_type = self_type or original_type\n self.context = context # Error context\n self.msg = msg\n self.chk = chk\n self.module_symbol_table = module_symbol_table\n\n def builtin_type(self, name: str) -> Instance:\n return self.chk.named_type(name)\n\n def not_ready_callback(self, name: str, context: Context) -> None:\n self.chk.handle_cannot_determine_type(name, context)\n\n def copy_modified(self, *, messages: Optional[MessageBuilder] = None,\n self_type: Optional[Type] = None) -> 'MemberContext':\n mx = MemberContext(self.is_lvalue, self.is_super, self.is_operator,\n self.original_type, self.context, self.msg, self.chk,\n self.self_type, self.module_symbol_table)\n if messages is not None:\n mx.msg = messages\n if self_type is not None:\n mx.self_type = self_type\n return mx\n\n\ndef analyze_member_access(name: str,\n typ: Type,\n context: Context,\n is_lvalue: bool,\n is_super: bool,\n is_operator: bool,\n msg: MessageBuilder, *,\n original_type: Type,\n chk: 'mypy.checker.TypeChecker',\n override_info: Optional[TypeInfo] = None,\n in_literal_context: bool = False,\n self_type: Optional[Type] = None,\n module_symbol_table: Optional[SymbolTable] = None) -> Type:\n \"\"\"Return the type of attribute 'name' of 'typ'.\n\n The actual implementation is in '_analyze_member_access' and this docstring\n also applies to it.\n\n This is a general operation that supports various different variations:\n\n 1. lvalue or non-lvalue access (setter or getter access)\n 2. supertype access when using super() (is_super == True and\n 'override_info' should refer to the supertype)\n\n 'original_type' is the most precise inferred or declared type of the base object\n that we have available. When looking for an attribute of 'typ', we may perform\n recursive calls targeting the fallback type, and 'typ' may become some supertype\n of 'original_type'. 'original_type' is always preserved as the 'typ' type used in\n the initial, non-recursive call. The 'self_type' is a component of 'original_type'\n to which generic self should be bound (a narrower type that has a fallback to instance).\n Currently this is used only for union types.\n\n 'module_symbol_table' is passed to this function if 'typ' is actually a module\n and we want to keep track of the available attributes of the module (since they\n are not available via the type object directly)\n \"\"\"\n mx = MemberContext(is_lvalue,\n is_super,\n is_operator,\n original_type,\n context,\n msg,\n chk=chk,\n self_type=self_type,\n module_symbol_table=module_symbol_table)\n result = _analyze_member_access(name, typ, mx, override_info)\n possible_literal = get_proper_type(result)\n if (in_literal_context and isinstance(possible_literal, Instance) and\n possible_literal.last_known_value is not None):\n return possible_literal.last_known_value\n else:\n return result\n\n\ndef _analyze_member_access(name: str,\n typ: Type,\n mx: MemberContext,\n override_info: Optional[TypeInfo] = None) -> Type:\n # TODO: This and following functions share some logic with subtypes.find_member;\n # consider refactoring.\n typ = get_proper_type(typ)\n if isinstance(typ, Instance):\n return analyze_instance_member_access(name, typ, mx, override_info)\n elif isinstance(typ, AnyType):\n # The base object has dynamic type.\n return AnyType(TypeOfAny.from_another_any, source_any=typ)\n elif isinstance(typ, UnionType):\n return analyze_union_member_access(name, typ, mx)\n elif isinstance(typ, FunctionLike) and typ.is_type_obj():\n return analyze_type_callable_member_access(name, typ, mx)\n elif isinstance(typ, TypeType):\n return analyze_type_type_member_access(name, typ, mx, override_info)\n elif isinstance(typ, TupleType):\n # Actually look up from the fallback instance type.\n return _analyze_member_access(name, tuple_fallback(typ), mx, override_info)\n elif isinstance(typ, (TypedDictType, LiteralType, FunctionLike)):\n # Actually look up from the fallback instance type.\n return _analyze_member_access(name, typ.fallback, mx, override_info)\n elif isinstance(typ, NoneType):\n return analyze_none_member_access(name, typ, mx)\n elif isinstance(typ, TypeVarType):\n return _analyze_member_access(name, typ.upper_bound, mx, override_info)\n elif isinstance(typ, DeletedType):\n mx.msg.deleted_as_rvalue(typ, mx.context)\n return AnyType(TypeOfAny.from_error)\n if mx.chk.should_suppress_optional_error([typ]):\n return AnyType(TypeOfAny.from_error)\n return mx.msg.has_no_attr(mx.original_type, typ, name, mx.context, mx.module_symbol_table)\n\n\n# The several functions that follow implement analyze_member_access for various\n# types and aren't documented individually.\n\n\ndef analyze_instance_member_access(name: str,\n typ: Instance,\n mx: MemberContext,\n override_info: Optional[TypeInfo]) -> Type:\n if name == '__init__' and not mx.is_super:\n # Accessing __init__ in statically typed code would compromise\n # type safety unless used via super().\n mx.msg.fail(message_registry.CANNOT_ACCESS_INIT, mx.context)\n return AnyType(TypeOfAny.from_error)\n\n # The base object has an instance type.\n\n info = typ.type\n if override_info:\n info = override_info\n\n if (state.find_occurrences and\n info.name == state.find_occurrences[0] and\n name == state.find_occurrences[1]):\n mx.msg.note(\"Occurrence of '{}.{}'\".format(*state.find_occurrences), mx.context)\n\n # Look up the member. First look up the method dictionary.\n method = info.get_method(name)\n if method:\n if method.is_property:\n assert isinstance(method, OverloadedFuncDef)\n first_item = cast(Decorator, method.items[0])\n return analyze_var(name, first_item.var, typ, info, mx)\n if mx.is_lvalue:\n mx.msg.cant_assign_to_method(mx.context)\n signature = function_type(method, mx.builtin_type('builtins.function'))\n signature = freshen_function_type_vars(signature)\n if name == '__new__':\n # __new__ is special and behaves like a static method -- don't strip\n # the first argument.\n pass\n else:\n if isinstance(signature, FunctionLike) and name != '__call__':\n # TODO: use proper treatment of special methods on unions instead\n # of this hack here and below (i.e. mx.self_type).\n dispatched_type = meet.meet_types(mx.original_type, typ)\n signature = check_self_arg(signature, dispatched_type, method.is_class,\n mx.context, name, mx.msg)\n signature = bind_self(signature, mx.self_type, is_classmethod=method.is_class)\n typ = map_instance_to_supertype(typ, method.info)\n member_type = expand_type_by_instance(signature, typ)\n freeze_type_vars(member_type)\n return member_type\n else:\n # Not a method.\n return analyze_member_var_access(name, typ, info, mx)\n\n\ndef analyze_type_callable_member_access(name: str,\n typ: FunctionLike,\n mx: MemberContext) -> Type:\n # Class attribute.\n # TODO super?\n ret_type = typ.items()[0].ret_type\n assert isinstance(ret_type, ProperType)\n if isinstance(ret_type, TupleType):\n ret_type = tuple_fallback(ret_type)\n if isinstance(ret_type, Instance):\n if not mx.is_operator:\n # When Python sees an operator (eg `3 == 4`), it automatically translates that\n # into something like `int.__eq__(3, 4)` instead of `(3).__eq__(4)` as an\n # optimization.\n #\n # While it normally it doesn't matter which of the two versions are used, it\n # does cause inconsistencies when working with classes. For example, translating\n # `int == int` to `int.__eq__(int)` would not work since `int.__eq__` is meant to\n # compare two int _instances_. What we really want is `type(int).__eq__`, which\n # is meant to compare two types or classes.\n #\n # This check makes sure that when we encounter an operator, we skip looking up\n # the corresponding method in the current instance to avoid this edge case.\n # See https://github.com/python/mypy/pull/1787 for more info.\n # TODO: do not rely on same type variables being present in all constructor overloads.\n result = analyze_class_attribute_access(ret_type, name, mx,\n original_vars=typ.items()[0].variables)\n if result:\n return result\n # Look up from the 'type' type.\n return _analyze_member_access(name, typ.fallback, mx)\n else:\n assert False, 'Unexpected type {}'.format(repr(ret_type))\n\n\ndef analyze_type_type_member_access(name: str,\n typ: TypeType,\n mx: MemberContext,\n override_info: Optional[TypeInfo]) -> Type:\n # Similar to analyze_type_callable_attribute_access.\n item = None\n fallback = mx.builtin_type('builtins.type')\n ignore_messages = mx.msg.copy()\n ignore_messages.disable_errors()\n if isinstance(typ.item, Instance):\n item = typ.item\n elif isinstance(typ.item, AnyType):\n mx = mx.copy_modified(messages=ignore_messages)\n return _analyze_member_access(name, fallback, mx, override_info)\n elif isinstance(typ.item, TypeVarType):\n upper_bound = get_proper_type(typ.item.upper_bound)\n if isinstance(upper_bound, Instance):\n item = upper_bound\n elif isinstance(upper_bound, TupleType):\n item = tuple_fallback(upper_bound)\n elif isinstance(upper_bound, AnyType):\n mx = mx.copy_modified(messages=ignore_messages)\n return _analyze_member_access(name, fallback, mx, override_info)\n elif isinstance(typ.item, TupleType):\n item = tuple_fallback(typ.item)\n elif isinstance(typ.item, FunctionLike) and typ.item.is_type_obj():\n item = typ.item.fallback\n elif isinstance(typ.item, TypeType):\n # Access member on metaclass object via Type[Type[C]]\n if isinstance(typ.item.item, Instance):\n item = typ.item.item.type.metaclass_type\n if item and not mx.is_operator:\n # See comment above for why operators are skipped\n result = analyze_class_attribute_access(item, name, mx, override_info)\n if result:\n if not (isinstance(get_proper_type(result), AnyType) and item.type.fallback_to_any):\n return result\n else:\n # We don't want errors on metaclass lookup for classes with Any fallback\n mx = mx.copy_modified(messages=ignore_messages)\n if item is not None:\n fallback = item.type.metaclass_type or fallback\n return _analyze_member_access(name, fallback, mx, override_info)\n\n\ndef analyze_union_member_access(name: str, typ: UnionType, mx: MemberContext) -> Type:\n mx.msg.disable_type_names += 1\n results = []\n for subtype in typ.relevant_items():\n # Self types should be bound to every individual item of a union.\n item_mx = mx.copy_modified(self_type=subtype)\n results.append(_analyze_member_access(name, subtype, item_mx))\n mx.msg.disable_type_names -= 1\n return make_simplified_union(results)\n\n\ndef analyze_none_member_access(name: str, typ: NoneType, mx: MemberContext) -> Type:\n if mx.chk.should_suppress_optional_error([typ]):\n return AnyType(TypeOfAny.from_error)\n is_python_3 = mx.chk.options.python_version[0] >= 3\n # In Python 2 \"None\" has exactly the same attributes as \"object\". Python 3 adds a single\n # extra attribute, \"__bool__\".\n if is_python_3 and name == '__bool__':\n return CallableType(arg_types=[],\n arg_kinds=[],\n arg_names=[],\n ret_type=mx.builtin_type('builtins.bool'),\n fallback=mx.builtin_type('builtins.function'))\n else:\n return _analyze_member_access(name, mx.builtin_type('builtins.object'), mx)\n\n\ndef analyze_member_var_access(name: str,\n itype: Instance,\n info: TypeInfo,\n mx: MemberContext) -> Type:\n \"\"\"Analyse attribute access that does not target a method.\n\n This is logically part of analyze_member_access and the arguments are similar.\n\n original_type is the type of E in the expression E.var\n \"\"\"\n # It was not a method. Try looking up a variable.\n v = lookup_member_var_or_accessor(info, name, mx.is_lvalue)\n\n vv = v\n if isinstance(vv, Decorator):\n # The associated Var node of a decorator contains the type.\n v = vv.var\n\n if isinstance(vv, TypeInfo):\n # If the associated variable is a TypeInfo synthesize a Var node for\n # the purposes of type checking. This enables us to type check things\n # like accessing class attributes on an inner class.\n v = Var(name, type=type_object_type(vv, mx.builtin_type))\n v.info = info\n\n if isinstance(vv, TypeAlias) and isinstance(get_proper_type(vv.target), Instance):\n # Similar to the above TypeInfo case, we allow using\n # qualified type aliases in runtime context if it refers to an\n # instance type. For example:\n # class C:\n # A = List[int]\n # x = C.A() <- this is OK\n typ = instance_alias_type(vv, mx.builtin_type)\n v = Var(name, type=typ)\n v.info = info\n\n if isinstance(v, Var):\n implicit = info[name].implicit\n\n # An assignment to final attribute is always an error,\n # independently of types.\n if mx.is_lvalue and not mx.chk.get_final_context():\n check_final_member(name, info, mx.msg, mx.context)\n\n return analyze_var(name, v, itype, info, mx, implicit=implicit)\n elif isinstance(v, FuncDef):\n assert False, \"Did not expect a function\"\n elif (not v and name not in ['__getattr__', '__setattr__', '__getattribute__'] and\n not mx.is_operator):\n if not mx.is_lvalue:\n for method_name in ('__getattribute__', '__getattr__'):\n method = info.get_method(method_name)\n # __getattribute__ is defined on builtins.object and returns Any, so without\n # the guard this search will always find object.__getattribute__ and conclude\n # that the attribute exists\n if method and method.info.fullname != 'builtins.object':\n function = function_type(method, mx.builtin_type('builtins.function'))\n bound_method = bind_self(function, mx.self_type)\n typ = map_instance_to_supertype(itype, method.info)\n getattr_type = get_proper_type(expand_type_by_instance(bound_method, typ))\n if isinstance(getattr_type, CallableType):\n result = getattr_type.ret_type\n\n # Call the attribute hook before returning.\n fullname = '{}.{}'.format(method.info.fullname, name)\n hook = mx.chk.plugin.get_attribute_hook(fullname)\n if hook:\n result = hook(AttributeContext(get_proper_type(mx.original_type),\n result, mx.context, mx.chk))\n return result\n else:\n setattr_meth = info.get_method('__setattr__')\n if setattr_meth and setattr_meth.info.fullname != 'builtins.object':\n setattr_func = function_type(setattr_meth, mx.builtin_type('builtins.function'))\n bound_type = bind_self(setattr_func, mx.self_type)\n typ = map_instance_to_supertype(itype, setattr_meth.info)\n setattr_type = get_proper_type(expand_type_by_instance(bound_type, typ))\n if isinstance(setattr_type, CallableType) and len(setattr_type.arg_types) > 0:\n return setattr_type.arg_types[-1]\n\n if itype.type.fallback_to_any:\n return AnyType(TypeOfAny.special_form)\n\n # Could not find the member.\n if mx.is_super:\n mx.msg.undefined_in_superclass(name, mx.context)\n return AnyType(TypeOfAny.from_error)\n else:\n if mx.chk and mx.chk.should_suppress_optional_error([itype]):\n return AnyType(TypeOfAny.from_error)\n return mx.msg.has_no_attr(\n mx.original_type, itype, name, mx.context, mx.module_symbol_table\n )\n\n\ndef check_final_member(name: str, info: TypeInfo, msg: MessageBuilder, ctx: Context) -> None:\n \"\"\"Give an error if the name being assigned was declared as final.\"\"\"\n for base in info.mro:\n sym = base.names.get(name)\n if sym and is_final_node(sym.node):\n msg.cant_assign_to_final(name, attr_assign=True, ctx=ctx)\n\n\ndef analyze_descriptor_access(instance_type: Type,\n descriptor_type: Type,\n builtin_type: Callable[[str], Instance],\n msg: MessageBuilder,\n context: Context, *,\n chk: 'mypy.checker.TypeChecker') -> Type:\n \"\"\"Type check descriptor access.\n\n Arguments:\n instance_type: The type of the instance on which the descriptor\n attribute is being accessed (the type of ``a`` in ``a.f`` when\n ``f`` is a descriptor).\n descriptor_type: The type of the descriptor attribute being accessed\n (the type of ``f`` in ``a.f`` when ``f`` is a descriptor).\n context: The node defining the context of this inference.\n Return:\n The return type of the appropriate ``__get__`` overload for the descriptor.\n \"\"\"\n instance_type = get_proper_type(instance_type)\n descriptor_type = get_proper_type(descriptor_type)\n\n if isinstance(descriptor_type, UnionType):\n # Map the access over union types\n return make_simplified_union([\n analyze_descriptor_access(instance_type, typ, builtin_type,\n msg, context, chk=chk)\n for typ in descriptor_type.items\n ])\n elif not isinstance(descriptor_type, Instance):\n return descriptor_type\n\n if not descriptor_type.type.has_readable_member('__get__'):\n return descriptor_type\n\n dunder_get = descriptor_type.type.get_method('__get__')\n\n if dunder_get is None:\n msg.fail(message_registry.DESCRIPTOR_GET_NOT_CALLABLE.format(descriptor_type), context)\n return AnyType(TypeOfAny.from_error)\n\n function = function_type(dunder_get, builtin_type('builtins.function'))\n bound_method = bind_self(function, descriptor_type)\n typ = map_instance_to_supertype(descriptor_type, dunder_get.info)\n dunder_get_type = expand_type_by_instance(bound_method, typ)\n\n if isinstance(instance_type, FunctionLike) and instance_type.is_type_obj():\n owner_type = instance_type.items()[0].ret_type\n instance_type = NoneType()\n elif isinstance(instance_type, TypeType):\n owner_type = instance_type.item\n instance_type = NoneType()\n else:\n owner_type = instance_type\n\n callable_name = chk.expr_checker.method_fullname(descriptor_type, \"__get__\")\n dunder_get_type = chk.expr_checker.transform_callee_type(\n callable_name, dunder_get_type,\n [TempNode(instance_type, context=context),\n TempNode(TypeType.make_normalized(owner_type), context=context)],\n [ARG_POS, ARG_POS], context, object_type=descriptor_type,\n )\n\n _, inferred_dunder_get_type = chk.expr_checker.check_call(\n dunder_get_type,\n [TempNode(instance_type, context=context),\n TempNode(TypeType.make_normalized(owner_type), context=context)],\n [ARG_POS, ARG_POS], context, object_type=descriptor_type,\n callable_name=callable_name)\n\n inferred_dunder_get_type = get_proper_type(inferred_dunder_get_type)\n if isinstance(inferred_dunder_get_type, AnyType):\n # check_call failed, and will have reported an error\n return inferred_dunder_get_type\n\n if not isinstance(inferred_dunder_get_type, CallableType):\n msg.fail(message_registry.DESCRIPTOR_GET_NOT_CALLABLE.format(descriptor_type), context)\n return AnyType(TypeOfAny.from_error)\n\n return inferred_dunder_get_type.ret_type\n\n\ndef instance_alias_type(alias: TypeAlias,\n builtin_type: Callable[[str], Instance]) -> Type:\n \"\"\"Type of a type alias node targeting an instance, when appears in runtime context.\n\n As usual, we first erase any unbound type variables to Any.\n \"\"\"\n target = get_proper_type(alias.target) # type: Type\n assert isinstance(get_proper_type(target),\n Instance), \"Must be called only with aliases to classes\"\n target = get_proper_type(set_any_tvars(alias, alias.line, alias.column))\n assert isinstance(target, Instance)\n tp = type_object_type(target.type, builtin_type)\n return expand_type_by_instance(tp, target)\n\n\ndef analyze_var(name: str,\n var: Var,\n itype: Instance,\n info: TypeInfo,\n mx: MemberContext, *,\n implicit: bool = False) -> Type:\n \"\"\"Analyze access to an attribute via a Var node.\n\n This is conceptually part of analyze_member_access and the arguments are similar.\n\n itype is the class object in which var is defined\n original_type is the type of E in the expression E.var\n if implicit is True, the original Var was created as an assignment to self\n \"\"\"\n # Found a member variable.\n itype = map_instance_to_supertype(itype, var.info)\n typ = var.type\n if typ:\n if isinstance(typ, PartialType):\n return mx.chk.handle_partial_var_type(typ, mx.is_lvalue, var, mx.context)\n if mx.is_lvalue and var.is_property and not var.is_settable_property:\n # TODO allow setting attributes in subclass (although it is probably an error)\n mx.msg.read_only_property(name, itype.type, mx.context)\n if mx.is_lvalue and var.is_classvar:\n mx.msg.cant_assign_to_classvar(name, mx.context)\n t = get_proper_type(expand_type_by_instance(typ, itype))\n result = t # type: Type\n typ = get_proper_type(typ)\n if var.is_initialized_in_class and isinstance(typ, FunctionLike) and not typ.is_type_obj():\n if mx.is_lvalue:\n if var.is_property:\n if not var.is_settable_property:\n mx.msg.read_only_property(name, itype.type, mx.context)\n else:\n mx.msg.cant_assign_to_method(mx.context)\n\n if not var.is_staticmethod:\n # Class-level function objects and classmethods become bound methods:\n # the former to the instance, the latter to the class.\n functype = typ\n # Use meet to narrow original_type to the dispatched type.\n # For example, assume\n # * A.f: Callable[[A1], None] where A1 <: A (maybe A1 == A)\n # * B.f: Callable[[B1], None] where B1 <: B (maybe B1 == B)\n # * x: Union[A1, B1]\n # In `x.f`, when checking `x` against A1 we assume x is compatible with A\n # and similarly for B1 when checking against B\n dispatched_type = meet.meet_types(mx.original_type, itype)\n signature = freshen_function_type_vars(functype)\n signature = check_self_arg(signature, dispatched_type, var.is_classmethod,\n mx.context, name, mx.msg)\n signature = bind_self(signature, mx.self_type, var.is_classmethod)\n expanded_signature = get_proper_type(expand_type_by_instance(signature, itype))\n freeze_type_vars(expanded_signature)\n if var.is_property:\n # A property cannot have an overloaded type => the cast is fine.\n assert isinstance(expanded_signature, CallableType)\n result = expanded_signature.ret_type\n else:\n result = expanded_signature\n else:\n if not var.is_ready:\n mx.not_ready_callback(var.name, mx.context)\n # Implicit 'Any' type.\n result = AnyType(TypeOfAny.special_form)\n fullname = '{}.{}'.format(var.info.fullname, name)\n hook = mx.chk.plugin.get_attribute_hook(fullname)\n if result and not mx.is_lvalue and not implicit:\n result = analyze_descriptor_access(mx.original_type, result, mx.builtin_type,\n mx.msg, mx.context, chk=mx.chk)\n if hook:\n result = hook(AttributeContext(get_proper_type(mx.original_type),\n result, mx.context, mx.chk))\n return result\n\n\ndef freeze_type_vars(member_type: Type) -> None:\n if not isinstance(member_type, ProperType):\n return\n if isinstance(member_type, CallableType):\n for v in member_type.variables:\n v.id.meta_level = 0\n if isinstance(member_type, Overloaded):\n for it in member_type.items():\n for v in it.variables:\n v.id.meta_level = 0\n\n\ndef lookup_member_var_or_accessor(info: TypeInfo, name: str,\n is_lvalue: bool) -> Optional[SymbolNode]:\n \"\"\"Find the attribute/accessor node that refers to a member of a type.\"\"\"\n # TODO handle lvalues\n node = info.get(name)\n if node:\n return node.node\n else:\n return None\n\n\ndef check_self_arg(functype: FunctionLike,\n dispatched_arg_type: Type,\n is_classmethod: bool,\n context: Context, name: str,\n msg: MessageBuilder) -> FunctionLike:\n \"\"\"Check that an instance has a valid type for a method with annotated 'self'.\n\n For example if the method is defined as:\n class A:\n def f(self: S) -> T: ...\n then for 'x.f' we check that meet(type(x), A) <: S. If the method is overloaded, we\n select only overloads items that satisfy this requirement. If there are no matching\n overloads, an error is generated.\n\n Note: dispatched_arg_type uses a meet to select a relevant item in case if the\n original type of 'x' is a union. This is done because several special methods\n treat union types in ad-hoc manner, so we can't use MemberContext.self_type yet.\n \"\"\"\n items = functype.items()\n if not items:\n return functype\n new_items = []\n if is_classmethod:\n dispatched_arg_type = TypeType.make_normalized(dispatched_arg_type)\n for item in items:\n if not item.arg_types or item.arg_kinds[0] not in (ARG_POS, ARG_STAR):\n # No positional first (self) argument (*args is okay).\n msg.no_formal_self(name, item, context)\n # This is pretty bad, so just return the original signature if\n # there is at least one such error.\n return functype\n else:\n selfarg = item.arg_types[0]\n if subtypes.is_subtype(dispatched_arg_type, erase_typevars(erase_to_bound(selfarg))):\n new_items.append(item)\n if not new_items:\n # Choose first item for the message (it may be not very helpful for overloads).\n msg.incompatible_self_argument(name, dispatched_arg_type, items[0],\n is_classmethod, context)\n return functype\n if len(new_items) == 1:\n return new_items[0]\n return Overloaded(new_items)\n\n\ndef analyze_class_attribute_access(itype: Instance,\n name: str,\n mx: MemberContext,\n override_info: Optional[TypeInfo] = None,\n original_vars: Optional[Sequence[TypeVarLikeDef]] = None\n ) -> Optional[Type]:\n \"\"\"Analyze access to an attribute on a class object.\n\n itype is the return type of the class object callable, original_type is the type\n of E in the expression E.var, original_vars are type variables of the class callable\n (for generic classes).\n \"\"\"\n info = itype.type\n if override_info:\n info = override_info\n\n node = info.get(name)\n if not node:\n if info.fallback_to_any:\n return AnyType(TypeOfAny.special_form)\n return None\n\n is_decorated = isinstance(node.node, Decorator)\n is_method = is_decorated or isinstance(node.node, FuncBase)\n if mx.is_lvalue:\n if is_method:\n mx.msg.cant_assign_to_method(mx.context)\n if isinstance(node.node, TypeInfo):\n mx.msg.fail(message_registry.CANNOT_ASSIGN_TO_TYPE, mx.context)\n\n # If a final attribute was declared on `self` in `__init__`, then it\n # can't be accessed on the class object.\n if node.implicit and isinstance(node.node, Var) and node.node.is_final:\n mx.msg.fail(message_registry.CANNOT_ACCESS_FINAL_INSTANCE_ATTR\n .format(node.node.name), mx.context)\n\n # An assignment to final attribute on class object is also always an error,\n # independently of types.\n if mx.is_lvalue and not mx.chk.get_final_context():\n check_final_member(name, info, mx.msg, mx.context)\n\n if info.is_enum and not (mx.is_lvalue or is_decorated or is_method):\n enum_class_attribute_type = analyze_enum_class_attribute_access(itype, name, mx)\n if enum_class_attribute_type:\n return enum_class_attribute_type\n\n t = node.type\n if t:\n if isinstance(t, PartialType):\n symnode = node.node\n assert isinstance(symnode, Var)\n return mx.chk.handle_partial_var_type(t, mx.is_lvalue, symnode, mx.context)\n\n # Find the class where method/variable was defined.\n if isinstance(node.node, Decorator):\n super_info = node.node.var.info # type: Optional[TypeInfo]\n elif isinstance(node.node, (Var, SYMBOL_FUNCBASE_TYPES)):\n super_info = node.node.info\n else:\n super_info = None\n\n # Map the type to how it would look as a defining class. For example:\n # class C(Generic[T]): ...\n # class D(C[Tuple[T, S]]): ...\n # D[int, str].method()\n # Here itype is D[int, str], isuper is C[Tuple[int, str]].\n if not super_info:\n isuper = None\n else:\n isuper = map_instance_to_supertype(itype, super_info)\n\n if isinstance(node.node, Var):\n assert isuper is not None\n # Check if original variable type has type variables. For example:\n # class C(Generic[T]):\n # x: T\n # C.x # Error, ambiguous access\n # C[int].x # Also an error, since C[int] is same as C at runtime\n if isinstance(t, TypeVarType) or has_type_vars(t):\n # Exception: access on Type[...], including first argument of class methods is OK.\n if not isinstance(get_proper_type(mx.original_type), TypeType) or node.implicit:\n if node.node.is_classvar:\n message = message_registry.GENERIC_CLASS_VAR_ACCESS\n else:\n message = message_registry.GENERIC_INSTANCE_VAR_CLASS_ACCESS\n mx.msg.fail(message, mx.context)\n\n # Erase non-mapped variables, but keep mapped ones, even if there is an error.\n # In the above example this means that we infer following types:\n # C.x -> Any\n # C[int].x -> int\n t = erase_typevars(expand_type_by_instance(t, isuper))\n\n is_classmethod = ((is_decorated and cast(Decorator, node.node).func.is_class)\n or (isinstance(node.node, FuncBase) and node.node.is_class))\n t = get_proper_type(t)\n if isinstance(t, FunctionLike) and is_classmethod:\n t = check_self_arg(t, mx.self_type, False, mx.context, name, mx.msg)\n result = add_class_tvars(t, isuper, is_classmethod,\n mx.self_type, original_vars=original_vars)\n if not mx.is_lvalue:\n result = analyze_descriptor_access(mx.original_type, result, mx.builtin_type,\n mx.msg, mx.context, chk=mx.chk)\n return result\n elif isinstance(node.node, Var):\n mx.not_ready_callback(name, mx.context)\n return AnyType(TypeOfAny.special_form)\n\n if isinstance(node.node, TypeVarExpr):\n mx.msg.fail(message_registry.CANNOT_USE_TYPEVAR_AS_EXPRESSION.format(\n info.name, name), mx.context)\n return AnyType(TypeOfAny.from_error)\n\n if isinstance(node.node, TypeInfo):\n return type_object_type(node.node, mx.builtin_type)\n\n if isinstance(node.node, MypyFile):\n # Reference to a module object.\n return mx.builtin_type('types.ModuleType')\n\n if (isinstance(node.node, TypeAlias) and\n isinstance(get_proper_type(node.node.target), Instance)):\n return instance_alias_type(node.node, mx.builtin_type)\n\n if is_decorated:\n assert isinstance(node.node, Decorator)\n if node.node.type:\n return node.node.type\n else:\n mx.not_ready_callback(name, mx.context)\n return AnyType(TypeOfAny.from_error)\n else:\n assert isinstance(node.node, FuncBase)\n typ = function_type(node.node, mx.builtin_type('builtins.function'))\n # Note: if we are accessing class method on class object, the cls argument is bound.\n # Annotated and/or explicit class methods go through other code paths above, for\n # unannotated implicit class methods we do this here.\n if node.node.is_class:\n typ = bind_self(typ, is_classmethod=True)\n return typ\n\n\ndef analyze_enum_class_attribute_access(itype: Instance,\n name: str,\n mx: MemberContext,\n ) -> Optional[Type]:\n # Skip \"_order_\" and \"__order__\", since Enum will remove it\n if name in (\"_order_\", \"__order__\"):\n return mx.msg.has_no_attr(\n mx.original_type, itype, name, mx.context, mx.module_symbol_table\n )\n # For other names surrendered by underscores, we don't make them Enum members\n if name.startswith('__') and name.endswith(\"__\") and name.replace('_', '') != '':\n return None\n\n enum_literal = LiteralType(name, fallback=itype)\n # When we analyze enums, the corresponding Instance is always considered to be erased\n # due to how the signature of Enum.__new__ is `(cls: Type[_T], value: object) -> _T`\n # in typeshed. However, this is really more of an implementation detail of how Enums\n # are typed, and we really don't want to treat every single Enum value as if it were\n # from type variable substitution. So we reset the 'erased' field here.\n return itype.copy_modified(erased=False, last_known_value=enum_literal)\n\n\ndef add_class_tvars(t: ProperType, isuper: Optional[Instance],\n is_classmethod: bool,\n original_type: Type,\n original_vars: Optional[Sequence[TypeVarLikeDef]] = None) -> Type:\n \"\"\"Instantiate type variables during analyze_class_attribute_access,\n e.g T and Q in the following:\n\n class A(Generic[T]):\n @classmethod\n def foo(cls: Type[Q]) -> Tuple[T, Q]: ...\n\n class B(A[str]): pass\n B.foo()\n\n Args:\n t: Declared type of the method (or property)\n isuper: Current instance mapped to the superclass where method was defined, this\n is usually done by map_instance_to_supertype()\n is_classmethod: True if this method is decorated with @classmethod\n original_type: The value of the type B in the expression B.foo() or the corresponding\n component in case of a union (this is used to bind the self-types)\n original_vars: Type variables of the class callable on which the method was accessed\n Returns:\n Expanded method type with added type variables (when needed).\n \"\"\"\n # TODO: verify consistency between Q and T\n\n # We add class type variables if the class method is accessed on class object\n # without applied type arguments, this matches the behavior of __init__().\n # For example (continuing the example in docstring):\n # A # The type of callable is def [T] () -> A[T], _not_ def () -> A[Any]\n # A[int] # The type of callable is def () -> A[int]\n # and\n # A.foo # The type is generic def [T] () -> Tuple[T, A[T]]\n # A[int].foo # The type is non-generic def () -> Tuple[int, A[int]]\n #\n # This behaviour is useful for defining alternative constructors for generic classes.\n # To achieve such behaviour, we add the class type variables that are still free\n # (i.e. appear in the return type of the class object on which the method was accessed).\n if isinstance(t, CallableType):\n tvars = original_vars if original_vars is not None else []\n if is_classmethod:\n t = freshen_function_type_vars(t)\n t = bind_self(t, original_type, is_classmethod=True)\n assert isuper is not None\n t = cast(CallableType, expand_type_by_instance(t, isuper))\n freeze_type_vars(t)\n return t.copy_modified(variables=list(tvars) + list(t.variables))\n elif isinstance(t, Overloaded):\n return Overloaded([cast(CallableType, add_class_tvars(item, isuper,\n is_classmethod, original_type,\n original_vars=original_vars))\n for item in t.items()])\n if isuper is not None:\n t = cast(ProperType, expand_type_by_instance(t, isuper))\n return t\n\n\ndef type_object_type(info: TypeInfo, builtin_type: Callable[[str], Instance]) -> ProperType:\n \"\"\"Return the type of a type object.\n\n For a generic type G with type variables T and S the type is generally of form\n\n Callable[..., G[T, S]]\n\n where ... are argument types for the __init__/__new__ method (without the self\n argument). Also, the fallback type will be 'type' instead of 'function'.\n \"\"\"\n\n # We take the type from whichever of __init__ and __new__ is first\n # in the MRO, preferring __init__ if there is a tie.\n init_method = info.get('__init__')\n new_method = info.get('__new__')\n if not init_method or not is_valid_constructor(init_method.node):\n # Must be an invalid class definition.\n return AnyType(TypeOfAny.from_error)\n # There *should* always be a __new__ method except the test stubs\n # lack it, so just copy init_method in that situation\n new_method = new_method or init_method\n if not is_valid_constructor(new_method.node):\n # Must be an invalid class definition.\n return AnyType(TypeOfAny.from_error)\n\n # The two is_valid_constructor() checks ensure this.\n assert isinstance(new_method.node, (SYMBOL_FUNCBASE_TYPES, Decorator))\n assert isinstance(init_method.node, (SYMBOL_FUNCBASE_TYPES, Decorator))\n\n init_index = info.mro.index(init_method.node.info)\n new_index = info.mro.index(new_method.node.info)\n\n fallback = info.metaclass_type or builtin_type('builtins.type')\n if init_index < new_index:\n method = init_method.node # type: Union[FuncBase, Decorator]\n is_new = False\n elif init_index > new_index:\n method = new_method.node\n is_new = True\n else:\n if init_method.node.info.fullname == 'builtins.object':\n # Both are defined by object. But if we've got a bogus\n # base class, we can't know for sure, so check for that.\n if info.fallback_to_any:\n # Construct a universal callable as the prototype.\n any_type = AnyType(TypeOfAny.special_form)\n sig = CallableType(arg_types=[any_type, any_type],\n arg_kinds=[ARG_STAR, ARG_STAR2],\n arg_names=[\"_args\", \"_kwds\"],\n ret_type=any_type,\n fallback=builtin_type('builtins.function'))\n return class_callable(sig, info, fallback, None, is_new=False)\n\n # Otherwise prefer __init__ in a tie. It isn't clear that this\n # is the right thing, but __new__ caused problems with\n # typeshed (#5647).\n method = init_method.node\n is_new = False\n # Construct callable type based on signature of __init__. Adjust\n # return type and insert type arguments.\n if isinstance(method, FuncBase):\n t = function_type(method, fallback)\n else:\n assert isinstance(method.type, ProperType)\n assert isinstance(method.type, FunctionLike) # is_valid_constructor() ensures this\n t = method.type\n return type_object_type_from_function(t, info, method.info, fallback, is_new)\n\n\ndef is_valid_constructor(n: Optional[SymbolNode]) -> bool:\n \"\"\"Does this node represents a valid constructor method?\n\n This includes normal functions, overloaded functions, and decorators\n that return a callable type.\n \"\"\"\n if isinstance(n, FuncBase):\n return True\n if isinstance(n, Decorator):\n return isinstance(get_proper_type(n.type), FunctionLike)\n return False\n","path":"mypy/checkmember.py"}],"string":"[\n {\n \"content\": \"\\\"\\\"\\\"Type checking of attribute access\\\"\\\"\\\"\\n\\nfrom typing import cast, Callable, Optional, Union, Sequence\\nfrom typing_extensions import TYPE_CHECKING\\n\\nfrom mypy.types import (\\n Type, Instance, AnyType, TupleType, TypedDictType, CallableType, FunctionLike,\\n TypeVarLikeDef, Overloaded, TypeVarType, UnionType, PartialType, TypeOfAny, LiteralType,\\n DeletedType, NoneType, TypeType, has_type_vars, get_proper_type, ProperType\\n)\\nfrom mypy.nodes import (\\n TypeInfo, FuncBase, Var, FuncDef, SymbolNode, SymbolTable, Context,\\n MypyFile, TypeVarExpr, ARG_POS, ARG_STAR, ARG_STAR2, Decorator,\\n OverloadedFuncDef, TypeAlias, TempNode, is_final_node,\\n SYMBOL_FUNCBASE_TYPES,\\n)\\nfrom mypy.messages import MessageBuilder\\nfrom mypy.maptype import map_instance_to_supertype\\nfrom mypy.expandtype import expand_type_by_instance, freshen_function_type_vars\\nfrom mypy.erasetype import erase_typevars\\nfrom mypy.plugin import AttributeContext\\nfrom mypy.typeanal import set_any_tvars\\nfrom mypy import message_registry\\nfrom mypy import subtypes\\nfrom mypy import meet\\nfrom mypy.typeops import (\\n tuple_fallback, bind_self, erase_to_bound, class_callable, type_object_type_from_function,\\n make_simplified_union, function_type,\\n)\\n\\nif TYPE_CHECKING: # import for forward declaration only\\n import mypy.checker\\n\\nfrom mypy import state\\n\\n\\nclass MemberContext:\\n \\\"\\\"\\\"Information and objects needed to type check attribute access.\\n\\n Look at the docstring of analyze_member_access for more information.\\n \\\"\\\"\\\"\\n\\n def __init__(self,\\n is_lvalue: bool,\\n is_super: bool,\\n is_operator: bool,\\n original_type: Type,\\n context: Context,\\n msg: MessageBuilder,\\n chk: 'mypy.checker.TypeChecker',\\n self_type: Optional[Type],\\n module_symbol_table: Optional[SymbolTable] = None) -> None:\\n self.is_lvalue = is_lvalue\\n self.is_super = is_super\\n self.is_operator = is_operator\\n self.original_type = original_type\\n self.self_type = self_type or original_type\\n self.context = context # Error context\\n self.msg = msg\\n self.chk = chk\\n self.module_symbol_table = module_symbol_table\\n\\n def builtin_type(self, name: str) -> Instance:\\n return self.chk.named_type(name)\\n\\n def not_ready_callback(self, name: str, context: Context) -> None:\\n self.chk.handle_cannot_determine_type(name, context)\\n\\n def copy_modified(self, *, messages: Optional[MessageBuilder] = None,\\n self_type: Optional[Type] = None) -> 'MemberContext':\\n mx = MemberContext(self.is_lvalue, self.is_super, self.is_operator,\\n self.original_type, self.context, self.msg, self.chk,\\n self.self_type, self.module_symbol_table)\\n if messages is not None:\\n mx.msg = messages\\n if self_type is not None:\\n mx.self_type = self_type\\n return mx\\n\\n\\ndef analyze_member_access(name: str,\\n typ: Type,\\n context: Context,\\n is_lvalue: bool,\\n is_super: bool,\\n is_operator: bool,\\n msg: MessageBuilder, *,\\n original_type: Type,\\n chk: 'mypy.checker.TypeChecker',\\n override_info: Optional[TypeInfo] = None,\\n in_literal_context: bool = False,\\n self_type: Optional[Type] = None,\\n module_symbol_table: Optional[SymbolTable] = None) -> Type:\\n \\\"\\\"\\\"Return the type of attribute 'name' of 'typ'.\\n\\n The actual implementation is in '_analyze_member_access' and this docstring\\n also applies to it.\\n\\n This is a general operation that supports various different variations:\\n\\n 1. lvalue or non-lvalue access (setter or getter access)\\n 2. supertype access when using super() (is_super == True and\\n 'override_info' should refer to the supertype)\\n\\n 'original_type' is the most precise inferred or declared type of the base object\\n that we have available. When looking for an attribute of 'typ', we may perform\\n recursive calls targeting the fallback type, and 'typ' may become some supertype\\n of 'original_type'. 'original_type' is always preserved as the 'typ' type used in\\n the initial, non-recursive call. The 'self_type' is a component of 'original_type'\\n to which generic self should be bound (a narrower type that has a fallback to instance).\\n Currently this is used only for union types.\\n\\n 'module_symbol_table' is passed to this function if 'typ' is actually a module\\n and we want to keep track of the available attributes of the module (since they\\n are not available via the type object directly)\\n \\\"\\\"\\\"\\n mx = MemberContext(is_lvalue,\\n is_super,\\n is_operator,\\n original_type,\\n context,\\n msg,\\n chk=chk,\\n self_type=self_type,\\n module_symbol_table=module_symbol_table)\\n result = _analyze_member_access(name, typ, mx, override_info)\\n possible_literal = get_proper_type(result)\\n if (in_literal_context and isinstance(possible_literal, Instance) and\\n possible_literal.last_known_value is not None):\\n return possible_literal.last_known_value\\n else:\\n return result\\n\\n\\ndef _analyze_member_access(name: str,\\n typ: Type,\\n mx: MemberContext,\\n override_info: Optional[TypeInfo] = None) -> Type:\\n # TODO: This and following functions share some logic with subtypes.find_member;\\n # consider refactoring.\\n typ = get_proper_type(typ)\\n if isinstance(typ, Instance):\\n return analyze_instance_member_access(name, typ, mx, override_info)\\n elif isinstance(typ, AnyType):\\n # The base object has dynamic type.\\n return AnyType(TypeOfAny.from_another_any, source_any=typ)\\n elif isinstance(typ, UnionType):\\n return analyze_union_member_access(name, typ, mx)\\n elif isinstance(typ, FunctionLike) and typ.is_type_obj():\\n return analyze_type_callable_member_access(name, typ, mx)\\n elif isinstance(typ, TypeType):\\n return analyze_type_type_member_access(name, typ, mx, override_info)\\n elif isinstance(typ, TupleType):\\n # Actually look up from the fallback instance type.\\n return _analyze_member_access(name, tuple_fallback(typ), mx, override_info)\\n elif isinstance(typ, (TypedDictType, LiteralType, FunctionLike)):\\n # Actually look up from the fallback instance type.\\n return _analyze_member_access(name, typ.fallback, mx, override_info)\\n elif isinstance(typ, NoneType):\\n return analyze_none_member_access(name, typ, mx)\\n elif isinstance(typ, TypeVarType):\\n return _analyze_member_access(name, typ.upper_bound, mx, override_info)\\n elif isinstance(typ, DeletedType):\\n mx.msg.deleted_as_rvalue(typ, mx.context)\\n return AnyType(TypeOfAny.from_error)\\n if mx.chk.should_suppress_optional_error([typ]):\\n return AnyType(TypeOfAny.from_error)\\n return mx.msg.has_no_attr(mx.original_type, typ, name, mx.context, mx.module_symbol_table)\\n\\n\\n# The several functions that follow implement analyze_member_access for various\\n# types and aren't documented individually.\\n\\n\\ndef analyze_instance_member_access(name: str,\\n typ: Instance,\\n mx: MemberContext,\\n override_info: Optional[TypeInfo]) -> Type:\\n if name == '__init__' and not mx.is_super:\\n # Accessing __init__ in statically typed code would compromise\\n # type safety unless used via super().\\n mx.msg.fail(message_registry.CANNOT_ACCESS_INIT, mx.context)\\n return AnyType(TypeOfAny.from_error)\\n\\n # The base object has an instance type.\\n\\n info = typ.type\\n if override_info:\\n info = override_info\\n\\n if (state.find_occurrences and\\n info.name == state.find_occurrences[0] and\\n name == state.find_occurrences[1]):\\n mx.msg.note(\\\"Occurrence of '{}.{}'\\\".format(*state.find_occurrences), mx.context)\\n\\n # Look up the member. First look up the method dictionary.\\n method = info.get_method(name)\\n if method:\\n if method.is_property:\\n assert isinstance(method, OverloadedFuncDef)\\n first_item = cast(Decorator, method.items[0])\\n return analyze_var(name, first_item.var, typ, info, mx)\\n if mx.is_lvalue:\\n mx.msg.cant_assign_to_method(mx.context)\\n signature = function_type(method, mx.builtin_type('builtins.function'))\\n signature = freshen_function_type_vars(signature)\\n if name == '__new__':\\n # __new__ is special and behaves like a static method -- don't strip\\n # the first argument.\\n pass\\n else:\\n if isinstance(signature, FunctionLike) and name != '__call__':\\n # TODO: use proper treatment of special methods on unions instead\\n # of this hack here and below (i.e. mx.self_type).\\n dispatched_type = meet.meet_types(mx.original_type, typ)\\n signature = check_self_arg(signature, dispatched_type, method.is_class,\\n mx.context, name, mx.msg)\\n signature = bind_self(signature, mx.self_type, is_classmethod=method.is_class)\\n typ = map_instance_to_supertype(typ, method.info)\\n member_type = expand_type_by_instance(signature, typ)\\n freeze_type_vars(member_type)\\n return member_type\\n else:\\n # Not a method.\\n return analyze_member_var_access(name, typ, info, mx)\\n\\n\\ndef analyze_type_callable_member_access(name: str,\\n typ: FunctionLike,\\n mx: MemberContext) -> Type:\\n # Class attribute.\\n # TODO super?\\n ret_type = typ.items()[0].ret_type\\n assert isinstance(ret_type, ProperType)\\n if isinstance(ret_type, TupleType):\\n ret_type = tuple_fallback(ret_type)\\n if isinstance(ret_type, Instance):\\n if not mx.is_operator:\\n # When Python sees an operator (eg `3 == 4`), it automatically translates that\\n # into something like `int.__eq__(3, 4)` instead of `(3).__eq__(4)` as an\\n # optimization.\\n #\\n # While it normally it doesn't matter which of the two versions are used, it\\n # does cause inconsistencies when working with classes. For example, translating\\n # `int == int` to `int.__eq__(int)` would not work since `int.__eq__` is meant to\\n # compare two int _instances_. What we really want is `type(int).__eq__`, which\\n # is meant to compare two types or classes.\\n #\\n # This check makes sure that when we encounter an operator, we skip looking up\\n # the corresponding method in the current instance to avoid this edge case.\\n # See https://github.com/python/mypy/pull/1787 for more info.\\n # TODO: do not rely on same type variables being present in all constructor overloads.\\n result = analyze_class_attribute_access(ret_type, name, mx,\\n original_vars=typ.items()[0].variables)\\n if result:\\n return result\\n # Look up from the 'type' type.\\n return _analyze_member_access(name, typ.fallback, mx)\\n else:\\n assert False, 'Unexpected type {}'.format(repr(ret_type))\\n\\n\\ndef analyze_type_type_member_access(name: str,\\n typ: TypeType,\\n mx: MemberContext,\\n override_info: Optional[TypeInfo]) -> Type:\\n # Similar to analyze_type_callable_attribute_access.\\n item = None\\n fallback = mx.builtin_type('builtins.type')\\n ignore_messages = mx.msg.copy()\\n ignore_messages.disable_errors()\\n if isinstance(typ.item, Instance):\\n item = typ.item\\n elif isinstance(typ.item, AnyType):\\n mx = mx.copy_modified(messages=ignore_messages)\\n return _analyze_member_access(name, fallback, mx, override_info)\\n elif isinstance(typ.item, TypeVarType):\\n upper_bound = get_proper_type(typ.item.upper_bound)\\n if isinstance(upper_bound, Instance):\\n item = upper_bound\\n elif isinstance(upper_bound, TupleType):\\n item = tuple_fallback(upper_bound)\\n elif isinstance(upper_bound, AnyType):\\n mx = mx.copy_modified(messages=ignore_messages)\\n return _analyze_member_access(name, fallback, mx, override_info)\\n elif isinstance(typ.item, TupleType):\\n item = tuple_fallback(typ.item)\\n elif isinstance(typ.item, FunctionLike) and typ.item.is_type_obj():\\n item = typ.item.fallback\\n elif isinstance(typ.item, TypeType):\\n # Access member on metaclass object via Type[Type[C]]\\n if isinstance(typ.item.item, Instance):\\n item = typ.item.item.type.metaclass_type\\n if item and not mx.is_operator:\\n # See comment above for why operators are skipped\\n result = analyze_class_attribute_access(item, name, mx, override_info)\\n if result:\\n if not (isinstance(get_proper_type(result), AnyType) and item.type.fallback_to_any):\\n return result\\n else:\\n # We don't want errors on metaclass lookup for classes with Any fallback\\n mx = mx.copy_modified(messages=ignore_messages)\\n if item is not None:\\n fallback = item.type.metaclass_type or fallback\\n return _analyze_member_access(name, fallback, mx, override_info)\\n\\n\\ndef analyze_union_member_access(name: str, typ: UnionType, mx: MemberContext) -> Type:\\n mx.msg.disable_type_names += 1\\n results = []\\n for subtype in typ.relevant_items():\\n # Self types should be bound to every individual item of a union.\\n item_mx = mx.copy_modified(self_type=subtype)\\n results.append(_analyze_member_access(name, subtype, item_mx))\\n mx.msg.disable_type_names -= 1\\n return make_simplified_union(results)\\n\\n\\ndef analyze_none_member_access(name: str, typ: NoneType, mx: MemberContext) -> Type:\\n if mx.chk.should_suppress_optional_error([typ]):\\n return AnyType(TypeOfAny.from_error)\\n is_python_3 = mx.chk.options.python_version[0] >= 3\\n # In Python 2 \\\"None\\\" has exactly the same attributes as \\\"object\\\". Python 3 adds a single\\n # extra attribute, \\\"__bool__\\\".\\n if is_python_3 and name == '__bool__':\\n return CallableType(arg_types=[],\\n arg_kinds=[],\\n arg_names=[],\\n ret_type=mx.builtin_type('builtins.bool'),\\n fallback=mx.builtin_type('builtins.function'))\\n else:\\n return _analyze_member_access(name, mx.builtin_type('builtins.object'), mx)\\n\\n\\ndef analyze_member_var_access(name: str,\\n itype: Instance,\\n info: TypeInfo,\\n mx: MemberContext) -> Type:\\n \\\"\\\"\\\"Analyse attribute access that does not target a method.\\n\\n This is logically part of analyze_member_access and the arguments are similar.\\n\\n original_type is the type of E in the expression E.var\\n \\\"\\\"\\\"\\n # It was not a method. Try looking up a variable.\\n v = lookup_member_var_or_accessor(info, name, mx.is_lvalue)\\n\\n vv = v\\n if isinstance(vv, Decorator):\\n # The associated Var node of a decorator contains the type.\\n v = vv.var\\n\\n if isinstance(vv, TypeInfo):\\n # If the associated variable is a TypeInfo synthesize a Var node for\\n # the purposes of type checking. This enables us to type check things\\n # like accessing class attributes on an inner class.\\n v = Var(name, type=type_object_type(vv, mx.builtin_type))\\n v.info = info\\n\\n if isinstance(vv, TypeAlias) and isinstance(get_proper_type(vv.target), Instance):\\n # Similar to the above TypeInfo case, we allow using\\n # qualified type aliases in runtime context if it refers to an\\n # instance type. For example:\\n # class C:\\n # A = List[int]\\n # x = C.A() <- this is OK\\n typ = instance_alias_type(vv, mx.builtin_type)\\n v = Var(name, type=typ)\\n v.info = info\\n\\n if isinstance(v, Var):\\n implicit = info[name].implicit\\n\\n # An assignment to final attribute is always an error,\\n # independently of types.\\n if mx.is_lvalue and not mx.chk.get_final_context():\\n check_final_member(name, info, mx.msg, mx.context)\\n\\n return analyze_var(name, v, itype, info, mx, implicit=implicit)\\n elif isinstance(v, FuncDef):\\n assert False, \\\"Did not expect a function\\\"\\n elif (not v and name not in ['__getattr__', '__setattr__', '__getattribute__'] and\\n not mx.is_operator):\\n if not mx.is_lvalue:\\n for method_name in ('__getattribute__', '__getattr__'):\\n method = info.get_method(method_name)\\n # __getattribute__ is defined on builtins.object and returns Any, so without\\n # the guard this search will always find object.__getattribute__ and conclude\\n # that the attribute exists\\n if method and method.info.fullname != 'builtins.object':\\n function = function_type(method, mx.builtin_type('builtins.function'))\\n bound_method = bind_self(function, mx.self_type)\\n typ = map_instance_to_supertype(itype, method.info)\\n getattr_type = get_proper_type(expand_type_by_instance(bound_method, typ))\\n if isinstance(getattr_type, CallableType):\\n result = getattr_type.ret_type\\n\\n # Call the attribute hook before returning.\\n fullname = '{}.{}'.format(method.info.fullname, name)\\n hook = mx.chk.plugin.get_attribute_hook(fullname)\\n if hook:\\n result = hook(AttributeContext(get_proper_type(mx.original_type),\\n result, mx.context, mx.chk))\\n return result\\n else:\\n setattr_meth = info.get_method('__setattr__')\\n if setattr_meth and setattr_meth.info.fullname != 'builtins.object':\\n setattr_func = function_type(setattr_meth, mx.builtin_type('builtins.function'))\\n bound_type = bind_self(setattr_func, mx.self_type)\\n typ = map_instance_to_supertype(itype, setattr_meth.info)\\n setattr_type = get_proper_type(expand_type_by_instance(bound_type, typ))\\n if isinstance(setattr_type, CallableType) and len(setattr_type.arg_types) > 0:\\n return setattr_type.arg_types[-1]\\n\\n if itype.type.fallback_to_any:\\n return AnyType(TypeOfAny.special_form)\\n\\n # Could not find the member.\\n if mx.is_super:\\n mx.msg.undefined_in_superclass(name, mx.context)\\n return AnyType(TypeOfAny.from_error)\\n else:\\n if mx.chk and mx.chk.should_suppress_optional_error([itype]):\\n return AnyType(TypeOfAny.from_error)\\n return mx.msg.has_no_attr(\\n mx.original_type, itype, name, mx.context, mx.module_symbol_table\\n )\\n\\n\\ndef check_final_member(name: str, info: TypeInfo, msg: MessageBuilder, ctx: Context) -> None:\\n \\\"\\\"\\\"Give an error if the name being assigned was declared as final.\\\"\\\"\\\"\\n for base in info.mro:\\n sym = base.names.get(name)\\n if sym and is_final_node(sym.node):\\n msg.cant_assign_to_final(name, attr_assign=True, ctx=ctx)\\n\\n\\ndef analyze_descriptor_access(instance_type: Type,\\n descriptor_type: Type,\\n builtin_type: Callable[[str], Instance],\\n msg: MessageBuilder,\\n context: Context, *,\\n chk: 'mypy.checker.TypeChecker') -> Type:\\n \\\"\\\"\\\"Type check descriptor access.\\n\\n Arguments:\\n instance_type: The type of the instance on which the descriptor\\n attribute is being accessed (the type of ``a`` in ``a.f`` when\\n ``f`` is a descriptor).\\n descriptor_type: The type of the descriptor attribute being accessed\\n (the type of ``f`` in ``a.f`` when ``f`` is a descriptor).\\n context: The node defining the context of this inference.\\n Return:\\n The return type of the appropriate ``__get__`` overload for the descriptor.\\n \\\"\\\"\\\"\\n instance_type = get_proper_type(instance_type)\\n descriptor_type = get_proper_type(descriptor_type)\\n\\n if isinstance(descriptor_type, UnionType):\\n # Map the access over union types\\n return make_simplified_union([\\n analyze_descriptor_access(instance_type, typ, builtin_type,\\n msg, context, chk=chk)\\n for typ in descriptor_type.items\\n ])\\n elif not isinstance(descriptor_type, Instance):\\n return descriptor_type\\n\\n if not descriptor_type.type.has_readable_member('__get__'):\\n return descriptor_type\\n\\n dunder_get = descriptor_type.type.get_method('__get__')\\n\\n if dunder_get is None:\\n msg.fail(message_registry.DESCRIPTOR_GET_NOT_CALLABLE.format(descriptor_type), context)\\n return AnyType(TypeOfAny.from_error)\\n\\n function = function_type(dunder_get, builtin_type('builtins.function'))\\n bound_method = bind_self(function, descriptor_type)\\n typ = map_instance_to_supertype(descriptor_type, dunder_get.info)\\n dunder_get_type = expand_type_by_instance(bound_method, typ)\\n\\n if isinstance(instance_type, FunctionLike) and instance_type.is_type_obj():\\n owner_type = instance_type.items()[0].ret_type\\n instance_type = NoneType()\\n elif isinstance(instance_type, TypeType):\\n owner_type = instance_type.item\\n instance_type = NoneType()\\n else:\\n owner_type = instance_type\\n\\n callable_name = chk.expr_checker.method_fullname(descriptor_type, \\\"__get__\\\")\\n dunder_get_type = chk.expr_checker.transform_callee_type(\\n callable_name, dunder_get_type,\\n [TempNode(instance_type, context=context),\\n TempNode(TypeType.make_normalized(owner_type), context=context)],\\n [ARG_POS, ARG_POS], context, object_type=descriptor_type,\\n )\\n\\n _, inferred_dunder_get_type = chk.expr_checker.check_call(\\n dunder_get_type,\\n [TempNode(instance_type, context=context),\\n TempNode(TypeType.make_normalized(owner_type), context=context)],\\n [ARG_POS, ARG_POS], context, object_type=descriptor_type,\\n callable_name=callable_name)\\n\\n inferred_dunder_get_type = get_proper_type(inferred_dunder_get_type)\\n if isinstance(inferred_dunder_get_type, AnyType):\\n # check_call failed, and will have reported an error\\n return inferred_dunder_get_type\\n\\n if not isinstance(inferred_dunder_get_type, CallableType):\\n msg.fail(message_registry.DESCRIPTOR_GET_NOT_CALLABLE.format(descriptor_type), context)\\n return AnyType(TypeOfAny.from_error)\\n\\n return inferred_dunder_get_type.ret_type\\n\\n\\ndef instance_alias_type(alias: TypeAlias,\\n builtin_type: Callable[[str], Instance]) -> Type:\\n \\\"\\\"\\\"Type of a type alias node targeting an instance, when appears in runtime context.\\n\\n As usual, we first erase any unbound type variables to Any.\\n \\\"\\\"\\\"\\n target = get_proper_type(alias.target) # type: Type\\n assert isinstance(get_proper_type(target),\\n Instance), \\\"Must be called only with aliases to classes\\\"\\n target = get_proper_type(set_any_tvars(alias, alias.line, alias.column))\\n assert isinstance(target, Instance)\\n tp = type_object_type(target.type, builtin_type)\\n return expand_type_by_instance(tp, target)\\n\\n\\ndef analyze_var(name: str,\\n var: Var,\\n itype: Instance,\\n info: TypeInfo,\\n mx: MemberContext, *,\\n implicit: bool = False) -> Type:\\n \\\"\\\"\\\"Analyze access to an attribute via a Var node.\\n\\n This is conceptually part of analyze_member_access and the arguments are similar.\\n\\n itype is the class object in which var is defined\\n original_type is the type of E in the expression E.var\\n if implicit is True, the original Var was created as an assignment to self\\n \\\"\\\"\\\"\\n # Found a member variable.\\n itype = map_instance_to_supertype(itype, var.info)\\n typ = var.type\\n if typ:\\n if isinstance(typ, PartialType):\\n return mx.chk.handle_partial_var_type(typ, mx.is_lvalue, var, mx.context)\\n if mx.is_lvalue and var.is_property and not var.is_settable_property:\\n # TODO allow setting attributes in subclass (although it is probably an error)\\n mx.msg.read_only_property(name, itype.type, mx.context)\\n if mx.is_lvalue and var.is_classvar:\\n mx.msg.cant_assign_to_classvar(name, mx.context)\\n t = get_proper_type(expand_type_by_instance(typ, itype))\\n result = t # type: Type\\n typ = get_proper_type(typ)\\n if var.is_initialized_in_class and isinstance(typ, FunctionLike) and not typ.is_type_obj():\\n if mx.is_lvalue:\\n if var.is_property:\\n if not var.is_settable_property:\\n mx.msg.read_only_property(name, itype.type, mx.context)\\n else:\\n mx.msg.cant_assign_to_method(mx.context)\\n\\n if not var.is_staticmethod:\\n # Class-level function objects and classmethods become bound methods:\\n # the former to the instance, the latter to the class.\\n functype = typ\\n # Use meet to narrow original_type to the dispatched type.\\n # For example, assume\\n # * A.f: Callable[[A1], None] where A1 <: A (maybe A1 == A)\\n # * B.f: Callable[[B1], None] where B1 <: B (maybe B1 == B)\\n # * x: Union[A1, B1]\\n # In `x.f`, when checking `x` against A1 we assume x is compatible with A\\n # and similarly for B1 when checking against B\\n dispatched_type = meet.meet_types(mx.original_type, itype)\\n signature = freshen_function_type_vars(functype)\\n signature = check_self_arg(signature, dispatched_type, var.is_classmethod,\\n mx.context, name, mx.msg)\\n signature = bind_self(signature, mx.self_type, var.is_classmethod)\\n expanded_signature = get_proper_type(expand_type_by_instance(signature, itype))\\n freeze_type_vars(expanded_signature)\\n if var.is_property:\\n # A property cannot have an overloaded type => the cast is fine.\\n assert isinstance(expanded_signature, CallableType)\\n result = expanded_signature.ret_type\\n else:\\n result = expanded_signature\\n else:\\n if not var.is_ready:\\n mx.not_ready_callback(var.name, mx.context)\\n # Implicit 'Any' type.\\n result = AnyType(TypeOfAny.special_form)\\n fullname = '{}.{}'.format(var.info.fullname, name)\\n hook = mx.chk.plugin.get_attribute_hook(fullname)\\n if result and not mx.is_lvalue and not implicit:\\n result = analyze_descriptor_access(mx.original_type, result, mx.builtin_type,\\n mx.msg, mx.context, chk=mx.chk)\\n if hook:\\n result = hook(AttributeContext(get_proper_type(mx.original_type),\\n result, mx.context, mx.chk))\\n return result\\n\\n\\ndef freeze_type_vars(member_type: Type) -> None:\\n if not isinstance(member_type, ProperType):\\n return\\n if isinstance(member_type, CallableType):\\n for v in member_type.variables:\\n v.id.meta_level = 0\\n if isinstance(member_type, Overloaded):\\n for it in member_type.items():\\n for v in it.variables:\\n v.id.meta_level = 0\\n\\n\\ndef lookup_member_var_or_accessor(info: TypeInfo, name: str,\\n is_lvalue: bool) -> Optional[SymbolNode]:\\n \\\"\\\"\\\"Find the attribute/accessor node that refers to a member of a type.\\\"\\\"\\\"\\n # TODO handle lvalues\\n node = info.get(name)\\n if node:\\n return node.node\\n else:\\n return None\\n\\n\\ndef check_self_arg(functype: FunctionLike,\\n dispatched_arg_type: Type,\\n is_classmethod: bool,\\n context: Context, name: str,\\n msg: MessageBuilder) -> FunctionLike:\\n \\\"\\\"\\\"Check that an instance has a valid type for a method with annotated 'self'.\\n\\n For example if the method is defined as:\\n class A:\\n def f(self: S) -> T: ...\\n then for 'x.f' we check that meet(type(x), A) <: S. If the method is overloaded, we\\n select only overloads items that satisfy this requirement. If there are no matching\\n overloads, an error is generated.\\n\\n Note: dispatched_arg_type uses a meet to select a relevant item in case if the\\n original type of 'x' is a union. This is done because several special methods\\n treat union types in ad-hoc manner, so we can't use MemberContext.self_type yet.\\n \\\"\\\"\\\"\\n items = functype.items()\\n if not items:\\n return functype\\n new_items = []\\n if is_classmethod:\\n dispatched_arg_type = TypeType.make_normalized(dispatched_arg_type)\\n for item in items:\\n if not item.arg_types or item.arg_kinds[0] not in (ARG_POS, ARG_STAR):\\n # No positional first (self) argument (*args is okay).\\n msg.no_formal_self(name, item, context)\\n # This is pretty bad, so just return the original signature if\\n # there is at least one such error.\\n return functype\\n else:\\n selfarg = item.arg_types[0]\\n if subtypes.is_subtype(dispatched_arg_type, erase_typevars(erase_to_bound(selfarg))):\\n new_items.append(item)\\n if not new_items:\\n # Choose first item for the message (it may be not very helpful for overloads).\\n msg.incompatible_self_argument(name, dispatched_arg_type, items[0],\\n is_classmethod, context)\\n return functype\\n if len(new_items) == 1:\\n return new_items[0]\\n return Overloaded(new_items)\\n\\n\\ndef analyze_class_attribute_access(itype: Instance,\\n name: str,\\n mx: MemberContext,\\n override_info: Optional[TypeInfo] = None,\\n original_vars: Optional[Sequence[TypeVarLikeDef]] = None\\n ) -> Optional[Type]:\\n \\\"\\\"\\\"Analyze access to an attribute on a class object.\\n\\n itype is the return type of the class object callable, original_type is the type\\n of E in the expression E.var, original_vars are type variables of the class callable\\n (for generic classes).\\n \\\"\\\"\\\"\\n info = itype.type\\n if override_info:\\n info = override_info\\n\\n node = info.get(name)\\n if not node:\\n if info.fallback_to_any:\\n return AnyType(TypeOfAny.special_form)\\n return None\\n\\n is_decorated = isinstance(node.node, Decorator)\\n is_method = is_decorated or isinstance(node.node, FuncBase)\\n if mx.is_lvalue:\\n if is_method:\\n mx.msg.cant_assign_to_method(mx.context)\\n if isinstance(node.node, TypeInfo):\\n mx.msg.fail(message_registry.CANNOT_ASSIGN_TO_TYPE, mx.context)\\n\\n # If a final attribute was declared on `self` in `__init__`, then it\\n # can't be accessed on the class object.\\n if node.implicit and isinstance(node.node, Var) and node.node.is_final:\\n mx.msg.fail(message_registry.CANNOT_ACCESS_FINAL_INSTANCE_ATTR\\n .format(node.node.name), mx.context)\\n\\n # An assignment to final attribute on class object is also always an error,\\n # independently of types.\\n if mx.is_lvalue and not mx.chk.get_final_context():\\n check_final_member(name, info, mx.msg, mx.context)\\n\\n if info.is_enum and not (mx.is_lvalue or is_decorated or is_method):\\n enum_class_attribute_type = analyze_enum_class_attribute_access(itype, name, mx)\\n if enum_class_attribute_type:\\n return enum_class_attribute_type\\n\\n t = node.type\\n if t:\\n if isinstance(t, PartialType):\\n symnode = node.node\\n assert isinstance(symnode, Var)\\n return mx.chk.handle_partial_var_type(t, mx.is_lvalue, symnode, mx.context)\\n\\n # Find the class where method/variable was defined.\\n if isinstance(node.node, Decorator):\\n super_info = node.node.var.info # type: Optional[TypeInfo]\\n elif isinstance(node.node, (Var, SYMBOL_FUNCBASE_TYPES)):\\n super_info = node.node.info\\n else:\\n super_info = None\\n\\n # Map the type to how it would look as a defining class. For example:\\n # class C(Generic[T]): ...\\n # class D(C[Tuple[T, S]]): ...\\n # D[int, str].method()\\n # Here itype is D[int, str], isuper is C[Tuple[int, str]].\\n if not super_info:\\n isuper = None\\n else:\\n isuper = map_instance_to_supertype(itype, super_info)\\n\\n if isinstance(node.node, Var):\\n assert isuper is not None\\n # Check if original variable type has type variables. For example:\\n # class C(Generic[T]):\\n # x: T\\n # C.x # Error, ambiguous access\\n # C[int].x # Also an error, since C[int] is same as C at runtime\\n if isinstance(t, TypeVarType) or has_type_vars(t):\\n # Exception: access on Type[...], including first argument of class methods is OK.\\n if not isinstance(get_proper_type(mx.original_type), TypeType) or node.implicit:\\n if node.node.is_classvar:\\n message = message_registry.GENERIC_CLASS_VAR_ACCESS\\n else:\\n message = message_registry.GENERIC_INSTANCE_VAR_CLASS_ACCESS\\n mx.msg.fail(message, mx.context)\\n\\n # Erase non-mapped variables, but keep mapped ones, even if there is an error.\\n # In the above example this means that we infer following types:\\n # C.x -> Any\\n # C[int].x -> int\\n t = erase_typevars(expand_type_by_instance(t, isuper))\\n\\n is_classmethod = ((is_decorated and cast(Decorator, node.node).func.is_class)\\n or (isinstance(node.node, FuncBase) and node.node.is_class))\\n t = get_proper_type(t)\\n if isinstance(t, FunctionLike) and is_classmethod:\\n t = check_self_arg(t, mx.self_type, False, mx.context, name, mx.msg)\\n result = add_class_tvars(t, isuper, is_classmethod,\\n mx.self_type, original_vars=original_vars)\\n if not mx.is_lvalue:\\n result = analyze_descriptor_access(mx.original_type, result, mx.builtin_type,\\n mx.msg, mx.context, chk=mx.chk)\\n return result\\n elif isinstance(node.node, Var):\\n mx.not_ready_callback(name, mx.context)\\n return AnyType(TypeOfAny.special_form)\\n\\n if isinstance(node.node, TypeVarExpr):\\n mx.msg.fail(message_registry.CANNOT_USE_TYPEVAR_AS_EXPRESSION.format(\\n info.name, name), mx.context)\\n return AnyType(TypeOfAny.from_error)\\n\\n if isinstance(node.node, TypeInfo):\\n return type_object_type(node.node, mx.builtin_type)\\n\\n if isinstance(node.node, MypyFile):\\n # Reference to a module object.\\n return mx.builtin_type('types.ModuleType')\\n\\n if (isinstance(node.node, TypeAlias) and\\n isinstance(get_proper_type(node.node.target), Instance)):\\n return instance_alias_type(node.node, mx.builtin_type)\\n\\n if is_decorated:\\n assert isinstance(node.node, Decorator)\\n if node.node.type:\\n return node.node.type\\n else:\\n mx.not_ready_callback(name, mx.context)\\n return AnyType(TypeOfAny.from_error)\\n else:\\n assert isinstance(node.node, FuncBase)\\n typ = function_type(node.node, mx.builtin_type('builtins.function'))\\n # Note: if we are accessing class method on class object, the cls argument is bound.\\n # Annotated and/or explicit class methods go through other code paths above, for\\n # unannotated implicit class methods we do this here.\\n if node.node.is_class:\\n typ = bind_self(typ, is_classmethod=True)\\n return typ\\n\\n\\ndef analyze_enum_class_attribute_access(itype: Instance,\\n name: str,\\n mx: MemberContext,\\n ) -> Optional[Type]:\\n # Skip \\\"_order_\\\" and \\\"__order__\\\", since Enum will remove it\\n if name in (\\\"_order_\\\", \\\"__order__\\\"):\\n return mx.msg.has_no_attr(\\n mx.original_type, itype, name, mx.context, mx.module_symbol_table\\n )\\n # For other names surrendered by underscores, we don't make them Enum members\\n if name.startswith('__') and name.endswith(\\\"__\\\") and name.replace('_', '') != '':\\n return None\\n\\n enum_literal = LiteralType(name, fallback=itype)\\n # When we analyze enums, the corresponding Instance is always considered to be erased\\n # due to how the signature of Enum.__new__ is `(cls: Type[_T], value: object) -> _T`\\n # in typeshed. However, this is really more of an implementation detail of how Enums\\n # are typed, and we really don't want to treat every single Enum value as if it were\\n # from type variable substitution. So we reset the 'erased' field here.\\n return itype.copy_modified(erased=False, last_known_value=enum_literal)\\n\\n\\ndef add_class_tvars(t: ProperType, isuper: Optional[Instance],\\n is_classmethod: bool,\\n original_type: Type,\\n original_vars: Optional[Sequence[TypeVarLikeDef]] = None) -> Type:\\n \\\"\\\"\\\"Instantiate type variables during analyze_class_attribute_access,\\n e.g T and Q in the following:\\n\\n class A(Generic[T]):\\n @classmethod\\n def foo(cls: Type[Q]) -> Tuple[T, Q]: ...\\n\\n class B(A[str]): pass\\n B.foo()\\n\\n Args:\\n t: Declared type of the method (or property)\\n isuper: Current instance mapped to the superclass where method was defined, this\\n is usually done by map_instance_to_supertype()\\n is_classmethod: True if this method is decorated with @classmethod\\n original_type: The value of the type B in the expression B.foo() or the corresponding\\n component in case of a union (this is used to bind the self-types)\\n original_vars: Type variables of the class callable on which the method was accessed\\n Returns:\\n Expanded method type with added type variables (when needed).\\n \\\"\\\"\\\"\\n # TODO: verify consistency between Q and T\\n\\n # We add class type variables if the class method is accessed on class object\\n # without applied type arguments, this matches the behavior of __init__().\\n # For example (continuing the example in docstring):\\n # A # The type of callable is def [T] () -> A[T], _not_ def () -> A[Any]\\n # A[int] # The type of callable is def () -> A[int]\\n # and\\n # A.foo # The type is generic def [T] () -> Tuple[T, A[T]]\\n # A[int].foo # The type is non-generic def () -> Tuple[int, A[int]]\\n #\\n # This behaviour is useful for defining alternative constructors for generic classes.\\n # To achieve such behaviour, we add the class type variables that are still free\\n # (i.e. appear in the return type of the class object on which the method was accessed).\\n if isinstance(t, CallableType):\\n tvars = original_vars if original_vars is not None else []\\n if is_classmethod:\\n t = freshen_function_type_vars(t)\\n t = bind_self(t, original_type, is_classmethod=True)\\n assert isuper is not None\\n t = cast(CallableType, expand_type_by_instance(t, isuper))\\n freeze_type_vars(t)\\n return t.copy_modified(variables=list(tvars) + list(t.variables))\\n elif isinstance(t, Overloaded):\\n return Overloaded([cast(CallableType, add_class_tvars(item, isuper,\\n is_classmethod, original_type,\\n original_vars=original_vars))\\n for item in t.items()])\\n if isuper is not None:\\n t = cast(ProperType, expand_type_by_instance(t, isuper))\\n return t\\n\\n\\ndef type_object_type(info: TypeInfo, builtin_type: Callable[[str], Instance]) -> ProperType:\\n \\\"\\\"\\\"Return the type of a type object.\\n\\n For a generic type G with type variables T and S the type is generally of form\\n\\n Callable[..., G[T, S]]\\n\\n where ... are argument types for the __init__/__new__ method (without the self\\n argument). Also, the fallback type will be 'type' instead of 'function'.\\n \\\"\\\"\\\"\\n\\n # We take the type from whichever of __init__ and __new__ is first\\n # in the MRO, preferring __init__ if there is a tie.\\n init_method = info.get('__init__')\\n new_method = info.get('__new__')\\n if not init_method or not is_valid_constructor(init_method.node):\\n # Must be an invalid class definition.\\n return AnyType(TypeOfAny.from_error)\\n # There *should* always be a __new__ method except the test stubs\\n # lack it, so just copy init_method in that situation\\n new_method = new_method or init_method\\n if not is_valid_constructor(new_method.node):\\n # Must be an invalid class definition.\\n return AnyType(TypeOfAny.from_error)\\n\\n # The two is_valid_constructor() checks ensure this.\\n assert isinstance(new_method.node, (SYMBOL_FUNCBASE_TYPES, Decorator))\\n assert isinstance(init_method.node, (SYMBOL_FUNCBASE_TYPES, Decorator))\\n\\n init_index = info.mro.index(init_method.node.info)\\n new_index = info.mro.index(new_method.node.info)\\n\\n fallback = info.metaclass_type or builtin_type('builtins.type')\\n if init_index < new_index:\\n method = init_method.node # type: Union[FuncBase, Decorator]\\n is_new = False\\n elif init_index > new_index:\\n method = new_method.node\\n is_new = True\\n else:\\n if init_method.node.info.fullname == 'builtins.object':\\n # Both are defined by object. But if we've got a bogus\\n # base class, we can't know for sure, so check for that.\\n if info.fallback_to_any:\\n # Construct a universal callable as the prototype.\\n any_type = AnyType(TypeOfAny.special_form)\\n sig = CallableType(arg_types=[any_type, any_type],\\n arg_kinds=[ARG_STAR, ARG_STAR2],\\n arg_names=[\\\"_args\\\", \\\"_kwds\\\"],\\n ret_type=any_type,\\n fallback=builtin_type('builtins.function'))\\n return class_callable(sig, info, fallback, None, is_new=False)\\n\\n # Otherwise prefer __init__ in a tie. It isn't clear that this\\n # is the right thing, but __new__ caused problems with\\n # typeshed (#5647).\\n method = init_method.node\\n is_new = False\\n # Construct callable type based on signature of __init__. Adjust\\n # return type and insert type arguments.\\n if isinstance(method, FuncBase):\\n t = function_type(method, fallback)\\n else:\\n assert isinstance(method.type, ProperType)\\n assert isinstance(method.type, FunctionLike) # is_valid_constructor() ensures this\\n t = method.type\\n return type_object_type_from_function(t, info, method.info, fallback, is_new)\\n\\n\\ndef is_valid_constructor(n: Optional[SymbolNode]) -> bool:\\n \\\"\\\"\\\"Does this node represents a valid constructor method?\\n\\n This includes normal functions, overloaded functions, and decorators\\n that return a callable type.\\n \\\"\\\"\\\"\\n if isinstance(n, FuncBase):\\n return True\\n if isinstance(n, Decorator):\\n return isinstance(get_proper_type(n.type), FunctionLike)\\n return False\\n\",\n \"path\": \"mypy/checkmember.py\"\n }\n]","isConversational":false},"pr_diff":{"kind":"string","value":"diff --git a/mypy/checkmember.py b/mypy/checkmember.py\nindex b9221958dacb..64e693d52c96 100644\n--- a/mypy/checkmember.py\n+++ b/mypy/checkmember.py\n@@ -280,6 +280,9 @@ def analyze_type_type_member_access(name: str,\n item = upper_bound\n elif isinstance(upper_bound, TupleType):\n item = tuple_fallback(upper_bound)\n+ elif isinstance(upper_bound, AnyType):\n+ mx = mx.copy_modified(messages=ignore_messages)\n+ return _analyze_member_access(name, fallback, mx, override_info)\n elif isinstance(typ.item, TupleType):\n item = tuple_fallback(typ.item)\n elif isinstance(typ.item, FunctionLike) and typ.item.is_type_obj():\ndiff --git a/test-data/unit/check-typevar-values.test b/test-data/unit/check-typevar-values.test\nindex 72993261a22f..affca2adab13 100644\n--- a/test-data/unit/check-typevar-values.test\n+++ b/test-data/unit/check-typevar-values.test\n@@ -598,6 +598,18 @@ class C:\n \n [builtins fixtures/list.pyi]\n \n+[case testTypeVarWithAnyTypeBound]\n+# flags: --follow-imports=skip\n+from typing import Type, TypeVar\n+from a import A\n+T = TypeVar('T', bound=A)\n+def method(t: Type[T]) -> None:\n+ t.a\n+[file a.py]\n+class A:\n+ a: int = 7\n+[out]\n+\n [case testParameterLessGenericAsRestriction]\n from typing import Sequence, Iterable, TypeVar\n S = TypeVar('S', Sequence, Iterable)\n"}}},{"rowIdx":10598,"cells":{"in_source_id":{"kind":"string","value":"MycroftAI__mycroft-core-2338"},"issue":{"kind":"string","value":"Skill Shutdown sometimes fails causing duplicate instances of Skill\n## Devices:\r\nIssue occurred on two separate Debian Linux installations\r\n- one running Mycroft-core 19.02.14, \r\n- the other running dev branch the date of this issue.\r\n\r\n## Steps to reproduce:\r\n1. Load Mycroft and ensure working as expected\r\n2. Make intentional code error in Alarm Skill to raise exception eg `self.log.info(not_a_real_var)`\r\n3. Skill fails on reload with the following error:\r\n```\r\n 12:04:25.758 | INFO | 22386 | mycroft.skills.skill_loader:reload:109 | ATTEMPTING TO RELOAD SKILL: mycroft-alarm.mycroftai\r\n 12:04:25.760 | ERROR | 22386 | mycroft.skills.skill_loader:_execute_instance_shutdown:145 | An error occurred while shutting down AlarmSkill\r\nTraceback (most recent call last):\r\n File \"/home/fs-neriahbjato/Documents/Full_Scale/Mycroft/mycroft-core/mycroft/skills/skill_loader.py\", line 142, in _execute_instance_shutdown\r\n self.instance.default_shutdown()\r\n File \"/home/fs-neriahbjato/Documents/Full_Scale/Mycroft/mycroft-core/mycroft/skills/mycroft_skill/mycroft_skill.py\", line 1162, in default_shutdown\r\n self.event_scheduler.shutdown()\r\n File \"/home/fs-neriahbjato/Documents/Full_Scale/Mycroft/mycroft-core/mycroft/skills/event_scheduler.py\", line 433, in shutdown\r\n self.events.clear()\r\n File \"/home/fs-neriahbjato/Documents/Full_Scale/Mycroft/mycroft-core/mycroft/skills/mycroft_skill/event_container.py\", line 183, in clear\r\n self.bus.remove(e, f)\r\n File \"/home/fs-neriahbjato/Documents/Full_Scale/Mycroft/mycroft-core/mycroft/messagebus/client/client.py\", line 172, in remove\r\n self.emitter.remove_listener(event_name, func)\r\n File \"/home/fs-neriahbjato/Documents/Full_Scale/Mycroft/mycroft-core/mycroft/messagebus/client/threaded_event_emitter.py\", line 57, in remove_listener\r\n return super().remove_listener(event_name, func)\r\n File \"/home/fs-neriahbjato/Documents/Full_Scale/Mycroft/mycroft-core/.venv/lib/python3.6/site-packages/pyee/__init__.py\", line 205, in remove_listener\r\n self._events[event].pop(f)\r\nKeyError: <function create_basic_wrapper.<locals>.wrapper at 0x7f2a504781e0>\r\n```\r\n4. Remove bad code and save to reload Skill\r\n```\r\n 12:04:25.803 | INFO | 22386 | mycroft.skills.skill_loader:_communicate_load_status:273 | Skill mycroft-alarm.mycroftai loaded successfully\r\n```\r\n5. Trigger the Skill with an utterance eg \"what alarms are set\"\r\n6. Mycroft responds twice\r\n - once from the failed instance of the Skill: \"An error occurred while processing a request in Alarm Skill\"\r\n - once from the new working instance of the Skill: \"No alarms have been set\"\r\n\r\nThe failed instance of the Skill will remain until Mycroft services are restarted.\r\nAfter restarting, the same steps cause the same error.\r\n\r\n## Investigation:\r\nThis was only occurring for the Alarm Skill, did not get the same behavior in the Date Time Skill.\r\nTo see what might be causing it, I added some logging to the offending function in [`mycroft/messagebus/client/threaded_event_emitter.py`](https://github.com/MycroftAI/mycroft-core/blob/dev/mycroft/messagebus/client/threaded_event_emitter.py):\r\n```\r\ndef remove_listener(self, event_name, func):\r\n \"\"\" Wrap the remove to translate from function to wrapped\r\n function.\r\n \"\"\"\r\n for w in self.wrappers[event_name]:\r\n if w[0] == func:\r\n self.wrappers[event_name].remove(w)\r\n return super().remove_listener(event_name, w[1])\r\n # if no wrapper exists try removing the function\r\n LOG.info(\"REMOVING LISTENER\")\r\n LOG.info(\"event_name: \" + str(event_name))\r\n LOG.info(event_name)\r\n LOG.info(\"func: \" + str(func))\r\n LOG.info(func)\r\n return super().remove_listener(event_name, func)\r\n```\r\nThen reloaded Mycroft and can no longer trigger the issue.\r\n\r\n## Hypothesis\r\n- Presumably the `func` was not being found in `self.wrappers` allowing it to proceed to try removing the function itself on [line 57](https://github.com/MycroftAI/mycroft-core/blob/dev/mycroft/messagebus/client/threaded_event_emitter.py#L57) which then failed.\r\n- Possibly the function was modified or removed from `self.wrappers` causing it to not match?\r\n- Is `self.wrappers` cached between service restarts, and would editing this file have recreated it?\r\n\r\n\r\nAs I'm no longer able to reproduce the error it's difficult to continue debugging. So documenting the situation here in case anyone else runs into it and we can pick it back up.\r\n\r\nUnless anyone else has an idea of what might be happening?\n"},"before_files":{"kind":"list like","value":[{"content":"from inspect import signature\n\nfrom mycroft.messagebus.message import Message\nfrom mycroft.metrics import Stopwatch, report_timing\nfrom mycroft.util.log import LOG\n\nfrom ..skill_data import to_alnum\n\n\ndef unmunge_message(message, skill_id):\n \"\"\"Restore message keywords by removing the Letterified skill ID.\n Arguments:\n message (Message): Intent result message\n skill_id (str): skill identifier\n Returns:\n Message without clear keywords\n \"\"\"\n if isinstance(message, Message) and isinstance(message.data, dict):\n skill_id = to_alnum(skill_id)\n for key in list(message.data.keys()):\n if key.startswith(skill_id):\n # replace the munged key with the real one\n new_key = key[len(skill_id):]\n message.data[new_key] = message.data.pop(key)\n\n return message\n\n\ndef get_handler_name(handler):\n \"\"\"Name (including class if available) of handler function.\n\n Arguments:\n handler (function): Function to be named\n\n Returns:\n string: handler name as string\n \"\"\"\n if '__self__' in dir(handler) and 'name' in dir(handler.__self__):\n return handler.__self__.name + '.' + handler.__name__\n else:\n return handler.__name__\n\n\ndef create_wrapper(handler, skill_id, on_start, on_end, on_error):\n \"\"\"Create the default skill handler wrapper.\n\n This wrapper handles things like metrics, reporting handler start/stop\n and errors.\n handler (callable): method/function to call\n skill_id: skill_id for associated skill\n on_start (function): function to call before executing the handler\n on_end (function): function to call after executing the handler\n on_error (function): function to call for error reporting\n \"\"\"\n def wrapper(message):\n stopwatch = Stopwatch()\n try:\n message = unmunge_message(message, skill_id)\n if on_start:\n on_start(message)\n\n with stopwatch:\n if len(signature(handler).parameters) == 0:\n handler()\n else:\n handler(message)\n\n except Exception as e:\n if on_error:\n on_error(e)\n finally:\n if on_end:\n on_end(message)\n\n # Send timing metrics\n context = message.context\n if context and 'ident' in context:\n report_timing(context['ident'], 'skill_handler', stopwatch,\n {'handler': handler.__name__,\n 'skill_id': skill_id})\n return wrapper\n\n\ndef create_basic_wrapper(handler, on_error=None):\n \"\"\"Create the default skill handler wrapper.\n\n This wrapper handles things like metrics, reporting handler start/stop\n and errors.\n\n Arguments:\n handler (callable): method/function to call\n on_error (function): function to call to report error.\n\n Returns:\n Wrapped callable\n \"\"\"\n def wrapper(message):\n try:\n if len(signature(handler).parameters) == 0:\n handler()\n else:\n handler(message)\n except Exception as e:\n if on_error:\n on_error(e)\n\n return wrapper\n\n\nclass EventContainer:\n \"\"\"Container tracking messagbus handlers.\n\n This container tracks events added by a skill, allowing unregistering\n all events on shutdown.\n \"\"\"\n def __init__(self, bus=None):\n self.bus = bus\n self.events = []\n\n def set_bus(self, bus):\n self.bus = bus\n\n def add(self, name, handler, once=False):\n \"\"\"Create event handler for executing intent or other event.\n\n Arguments:\n name (string): IntentParser name\n handler (func): Method to call\n once (bool, optional): Event handler will be removed after it has\n been run once.\n \"\"\"\n def once_wrapper(message):\n # Remove registered one-time handler before invoking,\n # allowing them to re-schedule themselves.\n handler(message)\n self.remove(name)\n\n if handler:\n if once:\n self.bus.once(name, once_wrapper)\n else:\n self.bus.on(name, handler)\n self.events.append((name, handler))\n\n def remove(self, name):\n \"\"\"Removes an event from bus emitter and events list.\n\n Args:\n name (string): Name of Intent or Scheduler Event\n Returns:\n bool: True if found and removed, False if not found\n \"\"\"\n print(\"Removing event {}\".format(name))\n removed = False\n for _name, _handler in list(self.events):\n if name == _name:\n try:\n self.events.remove((_name, _handler))\n except ValueError:\n LOG.error('Failed to remove event {}'.format(name))\n pass\n removed = True\n\n # Because of function wrappers, the emitter doesn't always directly\n # hold the _handler function, it sometimes holds something like\n # 'wrapper(_handler)'. So a call like:\n # self.bus.remove(_name, _handler)\n # will not find it, leaving an event handler with that name left behind\n # waiting to fire if it is ever re-installed and triggered.\n # Remove all handlers with the given name, regardless of handler.\n if removed:\n self.bus.remove_all_listeners(name)\n return removed\n\n def __iter__(self):\n return iter(self.events)\n\n def clear(self):\n \"\"\"Unregister all registered handlers and clear the list of registered\n events.\n \"\"\"\n for e, f in self.events:\n self.bus.remove(e, f)\n self.events = [] # Remove reference to wrappers\n","path":"mycroft/skills/mycroft_skill/event_container.py"}],"string":"[\n {\n \"content\": \"from inspect import signature\\n\\nfrom mycroft.messagebus.message import Message\\nfrom mycroft.metrics import Stopwatch, report_timing\\nfrom mycroft.util.log import LOG\\n\\nfrom ..skill_data import to_alnum\\n\\n\\ndef unmunge_message(message, skill_id):\\n \\\"\\\"\\\"Restore message keywords by removing the Letterified skill ID.\\n Arguments:\\n message (Message): Intent result message\\n skill_id (str): skill identifier\\n Returns:\\n Message without clear keywords\\n \\\"\\\"\\\"\\n if isinstance(message, Message) and isinstance(message.data, dict):\\n skill_id = to_alnum(skill_id)\\n for key in list(message.data.keys()):\\n if key.startswith(skill_id):\\n # replace the munged key with the real one\\n new_key = key[len(skill_id):]\\n message.data[new_key] = message.data.pop(key)\\n\\n return message\\n\\n\\ndef get_handler_name(handler):\\n \\\"\\\"\\\"Name (including class if available) of handler function.\\n\\n Arguments:\\n handler (function): Function to be named\\n\\n Returns:\\n string: handler name as string\\n \\\"\\\"\\\"\\n if '__self__' in dir(handler) and 'name' in dir(handler.__self__):\\n return handler.__self__.name + '.' + handler.__name__\\n else:\\n return handler.__name__\\n\\n\\ndef create_wrapper(handler, skill_id, on_start, on_end, on_error):\\n \\\"\\\"\\\"Create the default skill handler wrapper.\\n\\n This wrapper handles things like metrics, reporting handler start/stop\\n and errors.\\n handler (callable): method/function to call\\n skill_id: skill_id for associated skill\\n on_start (function): function to call before executing the handler\\n on_end (function): function to call after executing the handler\\n on_error (function): function to call for error reporting\\n \\\"\\\"\\\"\\n def wrapper(message):\\n stopwatch = Stopwatch()\\n try:\\n message = unmunge_message(message, skill_id)\\n if on_start:\\n on_start(message)\\n\\n with stopwatch:\\n if len(signature(handler).parameters) == 0:\\n handler()\\n else:\\n handler(message)\\n\\n except Exception as e:\\n if on_error:\\n on_error(e)\\n finally:\\n if on_end:\\n on_end(message)\\n\\n # Send timing metrics\\n context = message.context\\n if context and 'ident' in context:\\n report_timing(context['ident'], 'skill_handler', stopwatch,\\n {'handler': handler.__name__,\\n 'skill_id': skill_id})\\n return wrapper\\n\\n\\ndef create_basic_wrapper(handler, on_error=None):\\n \\\"\\\"\\\"Create the default skill handler wrapper.\\n\\n This wrapper handles things like metrics, reporting handler start/stop\\n and errors.\\n\\n Arguments:\\n handler (callable): method/function to call\\n on_error (function): function to call to report error.\\n\\n Returns:\\n Wrapped callable\\n \\\"\\\"\\\"\\n def wrapper(message):\\n try:\\n if len(signature(handler).parameters) == 0:\\n handler()\\n else:\\n handler(message)\\n except Exception as e:\\n if on_error:\\n on_error(e)\\n\\n return wrapper\\n\\n\\nclass EventContainer:\\n \\\"\\\"\\\"Container tracking messagbus handlers.\\n\\n This container tracks events added by a skill, allowing unregistering\\n all events on shutdown.\\n \\\"\\\"\\\"\\n def __init__(self, bus=None):\\n self.bus = bus\\n self.events = []\\n\\n def set_bus(self, bus):\\n self.bus = bus\\n\\n def add(self, name, handler, once=False):\\n \\\"\\\"\\\"Create event handler for executing intent or other event.\\n\\n Arguments:\\n name (string): IntentParser name\\n handler (func): Method to call\\n once (bool, optional): Event handler will be removed after it has\\n been run once.\\n \\\"\\\"\\\"\\n def once_wrapper(message):\\n # Remove registered one-time handler before invoking,\\n # allowing them to re-schedule themselves.\\n handler(message)\\n self.remove(name)\\n\\n if handler:\\n if once:\\n self.bus.once(name, once_wrapper)\\n else:\\n self.bus.on(name, handler)\\n self.events.append((name, handler))\\n\\n def remove(self, name):\\n \\\"\\\"\\\"Removes an event from bus emitter and events list.\\n\\n Args:\\n name (string): Name of Intent or Scheduler Event\\n Returns:\\n bool: True if found and removed, False if not found\\n \\\"\\\"\\\"\\n print(\\\"Removing event {}\\\".format(name))\\n removed = False\\n for _name, _handler in list(self.events):\\n if name == _name:\\n try:\\n self.events.remove((_name, _handler))\\n except ValueError:\\n LOG.error('Failed to remove event {}'.format(name))\\n pass\\n removed = True\\n\\n # Because of function wrappers, the emitter doesn't always directly\\n # hold the _handler function, it sometimes holds something like\\n # 'wrapper(_handler)'. So a call like:\\n # self.bus.remove(_name, _handler)\\n # will not find it, leaving an event handler with that name left behind\\n # waiting to fire if it is ever re-installed and triggered.\\n # Remove all handlers with the given name, regardless of handler.\\n if removed:\\n self.bus.remove_all_listeners(name)\\n return removed\\n\\n def __iter__(self):\\n return iter(self.events)\\n\\n def clear(self):\\n \\\"\\\"\\\"Unregister all registered handlers and clear the list of registered\\n events.\\n \\\"\\\"\\\"\\n for e, f in self.events:\\n self.bus.remove(e, f)\\n self.events = [] # Remove reference to wrappers\\n\",\n \"path\": \"mycroft/skills/mycroft_skill/event_container.py\"\n }\n]","isConversational":false},"after_files":{"kind":"list like","value":[{"content":"from inspect import signature\n\nfrom mycroft.messagebus.message import Message\nfrom mycroft.metrics import Stopwatch, report_timing\nfrom mycroft.util.log import LOG\n\nfrom ..skill_data import to_alnum\n\n\ndef unmunge_message(message, skill_id):\n \"\"\"Restore message keywords by removing the Letterified skill ID.\n Arguments:\n message (Message): Intent result message\n skill_id (str): skill identifier\n Returns:\n Message without clear keywords\n \"\"\"\n if isinstance(message, Message) and isinstance(message.data, dict):\n skill_id = to_alnum(skill_id)\n for key in list(message.data.keys()):\n if key.startswith(skill_id):\n # replace the munged key with the real one\n new_key = key[len(skill_id):]\n message.data[new_key] = message.data.pop(key)\n\n return message\n\n\ndef get_handler_name(handler):\n \"\"\"Name (including class if available) of handler function.\n\n Arguments:\n handler (function): Function to be named\n\n Returns:\n string: handler name as string\n \"\"\"\n if '__self__' in dir(handler) and 'name' in dir(handler.__self__):\n return handler.__self__.name + '.' + handler.__name__\n else:\n return handler.__name__\n\n\ndef create_wrapper(handler, skill_id, on_start, on_end, on_error):\n \"\"\"Create the default skill handler wrapper.\n\n This wrapper handles things like metrics, reporting handler start/stop\n and errors.\n handler (callable): method/function to call\n skill_id: skill_id for associated skill\n on_start (function): function to call before executing the handler\n on_end (function): function to call after executing the handler\n on_error (function): function to call for error reporting\n \"\"\"\n def wrapper(message):\n stopwatch = Stopwatch()\n try:\n message = unmunge_message(message, skill_id)\n if on_start:\n on_start(message)\n\n with stopwatch:\n if len(signature(handler).parameters) == 0:\n handler()\n else:\n handler(message)\n\n except Exception as e:\n if on_error:\n on_error(e)\n finally:\n if on_end:\n on_end(message)\n\n # Send timing metrics\n context = message.context\n if context and 'ident' in context:\n report_timing(context['ident'], 'skill_handler', stopwatch,\n {'handler': handler.__name__,\n 'skill_id': skill_id})\n return wrapper\n\n\ndef create_basic_wrapper(handler, on_error=None):\n \"\"\"Create the default skill handler wrapper.\n\n This wrapper handles things like metrics, reporting handler start/stop\n and errors.\n\n Arguments:\n handler (callable): method/function to call\n on_error (function): function to call to report error.\n\n Returns:\n Wrapped callable\n \"\"\"\n def wrapper(message):\n try:\n if len(signature(handler).parameters) == 0:\n handler()\n else:\n handler(message)\n except Exception as e:\n if on_error:\n on_error(e)\n\n return wrapper\n\n\nclass EventContainer:\n \"\"\"Container tracking messagbus handlers.\n\n This container tracks events added by a skill, allowing unregistering\n all events on shutdown.\n \"\"\"\n def __init__(self, bus=None):\n self.bus = bus\n self.events = []\n\n def set_bus(self, bus):\n self.bus = bus\n\n def add(self, name, handler, once=False):\n \"\"\"Create event handler for executing intent or other event.\n\n Arguments:\n name (string): IntentParser name\n handler (func): Method to call\n once (bool, optional): Event handler will be removed after it has\n been run once.\n \"\"\"\n def once_wrapper(message):\n # Remove registered one-time handler before invoking,\n # allowing them to re-schedule themselves.\n handler(message)\n self.remove(name)\n\n if handler:\n if once:\n self.bus.once(name, once_wrapper)\n self.events.append((name, once_wrapper))\n else:\n self.bus.on(name, handler)\n self.events.append((name, handler))\n\n LOG.debug('Added event: {}'.format(name))\n\n def remove(self, name):\n \"\"\"Removes an event from bus emitter and events list.\n\n Args:\n name (string): Name of Intent or Scheduler Event\n Returns:\n bool: True if found and removed, False if not found\n \"\"\"\n print(\"Removing event {}\".format(name))\n removed = False\n for _name, _handler in list(self.events):\n if name == _name:\n try:\n self.events.remove((_name, _handler))\n except ValueError:\n LOG.error('Failed to remove event {}'.format(name))\n pass\n removed = True\n\n # Because of function wrappers, the emitter doesn't always directly\n # hold the _handler function, it sometimes holds something like\n # 'wrapper(_handler)'. So a call like:\n # self.bus.remove(_name, _handler)\n # will not find it, leaving an event handler with that name left behind\n # waiting to fire if it is ever re-installed and triggered.\n # Remove all handlers with the given name, regardless of handler.\n if removed:\n self.bus.remove_all_listeners(name)\n return removed\n\n def __iter__(self):\n return iter(self.events)\n\n def clear(self):\n \"\"\"Unregister all registered handlers and clear the list of registered\n events.\n \"\"\"\n for e, f in self.events:\n self.bus.remove(e, f)\n self.events = [] # Remove reference to wrappers\n","path":"mycroft/skills/mycroft_skill/event_container.py"}],"string":"[\n {\n \"content\": \"from inspect import signature\\n\\nfrom mycroft.messagebus.message import Message\\nfrom mycroft.metrics import Stopwatch, report_timing\\nfrom mycroft.util.log import LOG\\n\\nfrom ..skill_data import to_alnum\\n\\n\\ndef unmunge_message(message, skill_id):\\n \\\"\\\"\\\"Restore message keywords by removing the Letterified skill ID.\\n Arguments:\\n message (Message): Intent result message\\n skill_id (str): skill identifier\\n Returns:\\n Message without clear keywords\\n \\\"\\\"\\\"\\n if isinstance(message, Message) and isinstance(message.data, dict):\\n skill_id = to_alnum(skill_id)\\n for key in list(message.data.keys()):\\n if key.startswith(skill_id):\\n # replace the munged key with the real one\\n new_key = key[len(skill_id):]\\n message.data[new_key] = message.data.pop(key)\\n\\n return message\\n\\n\\ndef get_handler_name(handler):\\n \\\"\\\"\\\"Name (including class if available) of handler function.\\n\\n Arguments:\\n handler (function): Function to be named\\n\\n Returns:\\n string: handler name as string\\n \\\"\\\"\\\"\\n if '__self__' in dir(handler) and 'name' in dir(handler.__self__):\\n return handler.__self__.name + '.' + handler.__name__\\n else:\\n return handler.__name__\\n\\n\\ndef create_wrapper(handler, skill_id, on_start, on_end, on_error):\\n \\\"\\\"\\\"Create the default skill handler wrapper.\\n\\n This wrapper handles things like metrics, reporting handler start/stop\\n and errors.\\n handler (callable): method/function to call\\n skill_id: skill_id for associated skill\\n on_start (function): function to call before executing the handler\\n on_end (function): function to call after executing the handler\\n on_error (function): function to call for error reporting\\n \\\"\\\"\\\"\\n def wrapper(message):\\n stopwatch = Stopwatch()\\n try:\\n message = unmunge_message(message, skill_id)\\n if on_start:\\n on_start(message)\\n\\n with stopwatch:\\n if len(signature(handler).parameters) == 0:\\n handler()\\n else:\\n handler(message)\\n\\n except Exception as e:\\n if on_error:\\n on_error(e)\\n finally:\\n if on_end:\\n on_end(message)\\n\\n # Send timing metrics\\n context = message.context\\n if context and 'ident' in context:\\n report_timing(context['ident'], 'skill_handler', stopwatch,\\n {'handler': handler.__name__,\\n 'skill_id': skill_id})\\n return wrapper\\n\\n\\ndef create_basic_wrapper(handler, on_error=None):\\n \\\"\\\"\\\"Create the default skill handler wrapper.\\n\\n This wrapper handles things like metrics, reporting handler start/stop\\n and errors.\\n\\n Arguments:\\n handler (callable): method/function to call\\n on_error (function): function to call to report error.\\n\\n Returns:\\n Wrapped callable\\n \\\"\\\"\\\"\\n def wrapper(message):\\n try:\\n if len(signature(handler).parameters) == 0:\\n handler()\\n else:\\n handler(message)\\n except Exception as e:\\n if on_error:\\n on_error(e)\\n\\n return wrapper\\n\\n\\nclass EventContainer:\\n \\\"\\\"\\\"Container tracking messagbus handlers.\\n\\n This container tracks events added by a skill, allowing unregistering\\n all events on shutdown.\\n \\\"\\\"\\\"\\n def __init__(self, bus=None):\\n self.bus = bus\\n self.events = []\\n\\n def set_bus(self, bus):\\n self.bus = bus\\n\\n def add(self, name, handler, once=False):\\n \\\"\\\"\\\"Create event handler for executing intent or other event.\\n\\n Arguments:\\n name (string): IntentParser name\\n handler (func): Method to call\\n once (bool, optional): Event handler will be removed after it has\\n been run once.\\n \\\"\\\"\\\"\\n def once_wrapper(message):\\n # Remove registered one-time handler before invoking,\\n # allowing them to re-schedule themselves.\\n handler(message)\\n self.remove(name)\\n\\n if handler:\\n if once:\\n self.bus.once(name, once_wrapper)\\n self.events.append((name, once_wrapper))\\n else:\\n self.bus.on(name, handler)\\n self.events.append((name, handler))\\n\\n LOG.debug('Added event: {}'.format(name))\\n\\n def remove(self, name):\\n \\\"\\\"\\\"Removes an event from bus emitter and events list.\\n\\n Args:\\n name (string): Name of Intent or Scheduler Event\\n Returns:\\n bool: True if found and removed, False if not found\\n \\\"\\\"\\\"\\n print(\\\"Removing event {}\\\".format(name))\\n removed = False\\n for _name, _handler in list(self.events):\\n if name == _name:\\n try:\\n self.events.remove((_name, _handler))\\n except ValueError:\\n LOG.error('Failed to remove event {}'.format(name))\\n pass\\n removed = True\\n\\n # Because of function wrappers, the emitter doesn't always directly\\n # hold the _handler function, it sometimes holds something like\\n # 'wrapper(_handler)'. So a call like:\\n # self.bus.remove(_name, _handler)\\n # will not find it, leaving an event handler with that name left behind\\n # waiting to fire if it is ever re-installed and triggered.\\n # Remove all handlers with the given name, regardless of handler.\\n if removed:\\n self.bus.remove_all_listeners(name)\\n return removed\\n\\n def __iter__(self):\\n return iter(self.events)\\n\\n def clear(self):\\n \\\"\\\"\\\"Unregister all registered handlers and clear the list of registered\\n events.\\n \\\"\\\"\\\"\\n for e, f in self.events:\\n self.bus.remove(e, f)\\n self.events = [] # Remove reference to wrappers\\n\",\n \"path\": \"mycroft/skills/mycroft_skill/event_container.py\"\n }\n]","isConversational":false},"pr_diff":{"kind":"string","value":"diff --git a/mycroft/skills/mycroft_skill/event_container.py b/mycroft/skills/mycroft_skill/event_container.py\nindex fb444d005af0..623ae3f602d0 100644\n--- a/mycroft/skills/mycroft_skill/event_container.py\n+++ b/mycroft/skills/mycroft_skill/event_container.py\n@@ -138,9 +138,12 @@ def once_wrapper(message):\n if handler:\n if once:\n self.bus.once(name, once_wrapper)\n+ self.events.append((name, once_wrapper))\n else:\n self.bus.on(name, handler)\n- self.events.append((name, handler))\n+ self.events.append((name, handler))\n+\n+ LOG.debug('Added event: {}'.format(name))\n \n def remove(self, name):\n \"\"\"Removes an event from bus emitter and events list.\ndiff --git a/test/unittests/skills/test_event_container.py b/test/unittests/skills/test_event_container.py\nindex 20d63f0c8dce..272c4bd11d25 100644\n--- a/test/unittests/skills/test_event_container.py\n+++ b/test/unittests/skills/test_event_container.py\n@@ -32,12 +32,13 @@ def test_add(self):\n self.assertTrue(bus.on.called)\n \n # Test add single shot event handler\n+ len_before = len(container.events)\n container.add('test2', example_handler, once=True)\n+ self.assertEqual(len_before + 1, len(container.events))\n self.assertTrue(bus.once.called)\n \n # Verify correct content in event container\n self.assertTrue(('test1', example_handler) in container.events)\n- self.assertTrue(('test2', example_handler) in container.events)\n self.assertEqual(len(container.events), 2)\n \n def test_remove(self):\n"}}},{"rowIdx":10599,"cells":{"in_source_id":{"kind":"string","value":"huggingface__trl-743"},"issue":{"kind":"string","value":"Bug in dpo_trainer.py, prediction_step function\nI see the following code in dpo_trainer.py's prediction_step method:\r\n```python\r\n... ...\r\nlogits_dict = {\r\n \"logits_test/chosen\": metrics[\"logits_test/chosen\"],\r\n \"logits_test/rejected\": metrics[\"logits_test/rejected\"],\r\n}\r\n... ...\r\n```\r\nBut the keys and values are defined in get_batch_metrics function:\r\n```python\r\n... ...\r\nprefix = \"eval_\" if train_eval == \"eval\" else \"\"\r\nmetrics[f\"{prefix}rewards/chosen\"] = chosen_rewards.cpu().numpy().mean()\r\nmetrics[f\"{prefix}rewards/rejected\"] = rejected_rewards.cpu().numpy().mean()\r\nmetrics[f\"{prefix}rewards/accuracies\"] = reward_accuracies.cpu().numpy().mean()\r\nmetrics[f\"{prefix}rewards/margins\"] = (chosen_rewards - rejected_rewards).cpu().numpy().mean()\r\nmetrics[f\"{prefix}logps/rejected\"] = policy_rejected_logps.detach().cpu().numpy().mean()\r\nmetrics[f\"{prefix}logps/chosen\"] = policy_chosen_logps.detach().cpu().numpy().mean()\r\nmetrics[f\"{prefix}logits/rejected\"] = policy_rejected_logits.detach().cpu().numpy().mean()\r\nmetrics[f\"{prefix}logits/chosen\"] = policy_chosen_logits.detach().cpu().numpy().mean()\r\n... ...\r\n```\r\n\r\nThat is, the variable metrics doesn't have \"logits_test/chosen\"、\"logits_test/rejected\".\r\n\r\nI have run this code. For transformers framework, if the compute_metrics method is not defined, prediction_loss_only will be set to True and errors will be ignored. But if prediction_loss_only is forced to False, an error is indeed occurred.\r\nSo should we use the keys defined in get_batch_metrics when constructing logits_dict?\n"},"before_files":{"kind":"list like","value":[{"content":"# DPO Authors: Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, and Chelsea Finn 2023\n# Copyright 2023 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport warnings\nfrom collections import defaultdict\nfrom typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom datasets import Dataset\nfrom transformers import DataCollator, PreTrainedModel, PreTrainedTokenizerBase, Trainer, TrainingArguments\nfrom transformers.trainer_callback import TrainerCallback\n\nfrom ..import_utils import is_peft_available\nfrom ..models import create_reference_model\nfrom .utils import DPODataCollatorWithPadding, disable_dropout_in_model, pad_to_length\n\n\nif is_peft_available():\n from peft import PeftModel, get_peft_model, prepare_model_for_int8_training\n\n\nclass DPOTrainer(Trainer):\n r\"\"\"\n Initialize DPOTrainer.\n\n Args:\n model (`transformers.PreTrainedModel`):\n The model to train, preferably an `AutoModelForSequenceClassification`.\n ref_model (`PreTrainedModelWrapper`):\n Hugging Face transformer model with a casual language modelling head. Used for implicit reward computation and loss. If no\n reference model is provided, the trainer will create a reference model with the same architecture as the model to be optimized.\n beta (`float`, defaults to 0.1):\n The beta factor in DPO loss. Higher beta means less divergence from the initial policy.\n args (`transformers.TrainingArguments`):\n The arguments to use for training.\n data_collator (`transformers.DataCollator`):\n The data collator to use for training. If None is specified, the default data collator (`DPODataCollatorWithPadding`) will be used\n which will pad the sequences to the maximum length of the sequences in the batch, given a dataset of paired sequences.\n label_pad_token_id (`int`, defaults to `-100`):\n The label pad token id. This argument is required if you want to use the default data collator.\n padding_value (`int`, defaults to `0`):\n The padding value. This argument is required if you want to use the default data collator.\n truncation_mode (`str`, defaults to `keep_end`):\n The truncation mode to use, either `keep_end` or `keep_start`. This argument is required if you want to use the default data collator.\n train_dataset (`datasets.Dataset`):\n The dataset to use for training.\n eval_dataset (`datasets.Dataset`):\n The dataset to use for evaluation.\n tokenizer (`transformers.PreTrainedTokenizerBase`):\n The tokenizer to use for training. This argument is required if you want to use the default data collator.\n model_init (`Callable[[], transformers.PreTrainedModel]`):\n The model initializer to use for training. If None is specified, the default model initializer will be used.\n callbacks (`List[transformers.TrainerCallback]`):\n The callbacks to use for training.\n optimizers (`Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`):\n The optimizer and scheduler to use for training.\n preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`):\n The function to use to preprocess the logits before computing the metrics.\n max_length (`int`, defaults to `None`):\n The maximum length of the sequences in the batch. This argument is required if you want to use the default data collator.\n max_prompt_length (`int`, defaults to `None`):\n The maximum length of the prompt. This argument is required if you want to use the default data collator.\n peft_config (`Dict`, defaults to `None`):\n The PEFT configuration to use for training. If you pass a PEFT configuration, the model will be wrapped in a PEFT model.\n disable_dropout (`bool`, defaults to `True`):\n Whether or not to disable dropouts in `model` and `ref_model`.\n \"\"\"\n\n def __init__(\n self,\n model: Union[PreTrainedModel, nn.Module] = None,\n ref_model: Optional[Union[PreTrainedModel, nn.Module]] = None,\n beta: float = 0.1,\n args: TrainingArguments = None,\n data_collator: Optional[DataCollator] = None,\n label_pad_token_id: int = -100,\n padding_value: int = 0,\n truncation_mode: str = \"keep_end\",\n train_dataset: Optional[Dataset] = None,\n eval_dataset: Optional[Union[Dataset, Dict[str, Dataset]]] = None,\n tokenizer: Optional[PreTrainedTokenizerBase] = None,\n model_init: Optional[Callable[[], PreTrainedModel]] = None,\n callbacks: Optional[List[TrainerCallback]] = None,\n optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (\n None,\n None,\n ),\n preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,\n max_length: Optional[int] = None,\n max_prompt_length: Optional[int] = None,\n peft_config: Optional[Dict] = None,\n disable_dropout: bool = True,\n ):\n if not is_peft_available() and peft_config is not None:\n raise ValueError(\n \"PEFT is not installed and you passed a `peft_config` in the trainer's kwargs, please install it to use the PEFT models\"\n )\n elif is_peft_available() and peft_config is not None:\n if getattr(model, \"is_loaded_in_8bit\", False) or getattr(model, \"is_loaded_in_4bit\", False):\n model = prepare_model_for_int8_training(model)\n model = get_peft_model(model, peft_config)\n\n self.is_peft_model = is_peft_available() and isinstance(model, PeftModel)\n\n if ref_model:\n self.ref_model = ref_model\n elif self.is_peft_model:\n # The `model` with adapters turned off will be used as the reference model\n self.ref_model = None\n else:\n self.ref_model = create_reference_model(model)\n\n if data_collator is None:\n if tokenizer is None:\n raise ValueError(\n \"max_length or a tokenizer must be specified when using the default DPODataCollatorWithPadding\"\n )\n if max_length is None:\n warnings.warn(\n \"When using DPODataCollatorWithPadding, you should set `max_length` in the DPOTrainer's init\"\n \" it will be set to `512` by default, but you should do it yourself in the future.\",\n UserWarning,\n )\n max_length = 512\n if max_prompt_length is None:\n warnings.warn(\n \"When using DPODataCollatorWithPadding, you should set `max_prompt_length` in the DPOTrainer's init\"\n \" it will be set to `128` by default, but you should do it yourself in the future.\",\n UserWarning,\n )\n max_prompt_length = 128\n\n data_collator = DPODataCollatorWithPadding(\n tokenizer,\n max_length=max_length,\n max_prompt_length=max_prompt_length,\n label_pad_token_id=label_pad_token_id,\n padding_value=padding_value,\n truncation_mode=truncation_mode,\n )\n\n if args.remove_unused_columns:\n args.remove_unused_columns = False\n # warn users\n warnings.warn(\n \"When using DPODataCollatorWithPadding, you should set `remove_unused_columns=False` in your TrainingArguments\"\n \" we have set it for you, but you should do it yourself in the future.\",\n UserWarning,\n )\n\n self.use_dpo_data_collator = True\n else:\n self.use_dpo_data_collator = False\n\n if disable_dropout:\n disable_dropout_in_model(model)\n if self.ref_model is not None:\n disable_dropout_in_model(self.ref_model)\n\n self.label_pad_token_id = label_pad_token_id\n self.padding_value = padding_value\n\n self.beta = beta\n\n self._stored_metrics = defaultdict(lambda: defaultdict(list))\n\n super().__init__(\n model,\n args,\n data_collator,\n train_dataset,\n eval_dataset,\n tokenizer,\n model_init,\n None,\n callbacks,\n optimizers,\n preprocess_logits_for_metrics,\n )\n\n if not hasattr(self, \"accelerator\"):\n raise AttributeError(\n \"Your `Trainer` does not have an `accelerator` object. Consider upgrading `transformers`.\"\n )\n\n if self.ref_model is None:\n if not hasattr(self.accelerator.unwrap_model(self.model), \"disable_adapter\"):\n raise ValueError(\n \"You are using a `peft` version that does not support `disable_adapter`. Please update your `peft` version to the latest version.\"\n )\n else:\n if self.is_deepspeed_enabled:\n # Read more about the issue in https://github.com/huggingface/trl/pull/687\n self.ref_model = self.accelerator._prepare_deepspeed(self.ref_model)\n self.ref_model.eval()\n else:\n self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True)\n\n def concatenated_inputs(self, batch: Dict[str, Union[List, torch.LongTensor]]) -> Dict[str, torch.LongTensor]:\n \"\"\"Concatenate the chosen and rejected inputs into a single tensor.\n\n Args:\n batch: A batch of data. Must contain the keys 'chosen_input_ids' and 'rejected_input_ids', which are tensors of shape (batch_size, sequence_length).\n\n Returns:\n A dictionary containing the concatenated inputs under the key 'concatenated_input_ids'.\n \"\"\"\n max_length = max(batch[\"chosen_input_ids\"].shape[1], batch[\"rejected_input_ids\"].shape[1])\n concatenated_batch = {}\n for k in batch:\n if k.startswith(\"chosen\") and isinstance(batch[k], torch.Tensor):\n pad_value = self.label_pad_token_id if \"labels\" in k else self.padding_value\n concatenated_key = k.replace(\"chosen\", \"concatenated\")\n concatenated_batch[concatenated_key] = pad_to_length(batch[k], max_length, pad_value=pad_value)\n for k in batch:\n if k.startswith(\"rejected\") and isinstance(batch[k], torch.Tensor):\n pad_value = self.label_pad_token_id if \"labels\" in k else self.padding_value\n concatenated_key = k.replace(\"rejected\", \"concatenated\")\n concatenated_batch[concatenated_key] = torch.cat(\n (\n concatenated_batch[concatenated_key],\n pad_to_length(batch[k], max_length, pad_value=pad_value),\n ),\n dim=0,\n ).to(self.accelerator.device)\n return concatenated_batch\n\n def dpo_loss(\n self,\n policy_chosen_logps: torch.FloatTensor,\n policy_rejected_logps: torch.FloatTensor,\n reference_chosen_logps: torch.FloatTensor,\n reference_rejected_logps: torch.FloatTensor,\n reference_free: bool = False,\n ) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:\n \"\"\"Compute the DPO loss for a batch of policy and reference model log probabilities.\n\n Args:\n policy_chosen_logps: Log probabilities of the policy model for the chosen responses. Shape: (batch_size,)\n policy_rejected_logps: Log probabilities of the policy model for the rejected responses. Shape: (batch_size,)\n reference_chosen_logps: Log probabilities of the reference model for the chosen responses. Shape: (batch_size,)\n reference_rejected_logps: Log probabilities of the reference model for the rejected responses. Shape: (batch_size,)\n beta: Temperature parameter for the DPO loss, typically something in the range of 0.1 to 0.5. We ignore the reference model as beta -> 0.\n reference_free: If True, we ignore the _provided_ reference model and implicitly use a reference model that assigns equal probability to all responses.\n\n Returns:\n A tuple of three tensors: (losses, chosen_rewards, rejected_rewards).\n The losses tensor contains the DPO loss for each example in the batch.\n The chosen_rewards and rejected_rewards tensors contain the rewards for the chosen and rejected responses, respectively.\n \"\"\"\n pi_logratios = policy_chosen_logps - policy_rejected_logps\n ref_logratios = reference_chosen_logps - reference_rejected_logps\n\n if reference_free:\n ref_logratios = 0\n\n logits = pi_logratios - ref_logratios\n\n losses = -F.logsigmoid(self.beta * logits)\n chosen_rewards = self.beta * (policy_chosen_logps - reference_chosen_logps).detach()\n rejected_rewards = self.beta * (policy_rejected_logps - reference_rejected_logps).detach()\n\n return losses, chosen_rewards, rejected_rewards\n\n def _get_batch_logps(\n self,\n logits: torch.FloatTensor,\n labels: torch.LongTensor,\n average_log_prob: bool = False,\n ) -> torch.FloatTensor:\n \"\"\"Compute the log probabilities of the given labels under the given logits.\n\n Args:\n logits: Logits of the model (unnormalized). Shape: (batch_size, sequence_length, vocab_size)\n labels: Labels for which to compute the log probabilities. Label tokens with a value of label_pad_token_id are ignored. Shape: (batch_size, sequence_length)\n average_log_prob: If True, return the average log probability per (non-masked) token. Otherwise, return the sum of the log probabilities of the (non-masked) tokens.\n\n Returns:\n A tensor of shape (batch_size,) containing the average/sum log probabilities of the given labels under the given logits.\n \"\"\"\n if logits.shape[:-1] != labels.shape:\n raise ValueError(\"Logits (batch and sequence length dim) and labels must have the same shape.\")\n\n labels = labels[:, 1:].clone()\n logits = logits[:, :-1, :]\n loss_mask = labels != self.label_pad_token_id\n\n # dummy token; we'll ignore the losses on these tokens later\n labels[labels == self.label_pad_token_id] = 0\n\n per_token_logps = torch.gather(logits.log_softmax(-1), dim=2, index=labels.unsqueeze(2)).squeeze(2)\n\n if average_log_prob:\n return (per_token_logps * loss_mask).sum(-1) / loss_mask.sum(-1)\n else:\n return (per_token_logps * loss_mask).sum(-1)\n\n def concatenated_forward(\n self, model: nn.Module, batch: Dict[str, Union[List, torch.LongTensor]]\n ) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:\n \"\"\"Run the given model on the given batch of inputs, concatenating the chosen and rejected inputs together.\n\n We do this to avoid doing two forward passes, because it's faster for FSDP.\n \"\"\"\n concatenated_batch = self.concatenated_inputs(batch)\n all_logits = model(\n concatenated_batch[\"concatenated_input_ids\"],\n attention_mask=concatenated_batch[\"concatenated_attention_mask\"],\n ).logits.to(torch.float32)\n all_logps = self._get_batch_logps(\n all_logits,\n concatenated_batch[\"concatenated_labels\"],\n average_log_prob=False,\n )\n chosen_logps = all_logps[: batch[\"chosen_input_ids\"].shape[0]]\n rejected_logps = all_logps[batch[\"chosen_input_ids\"].shape[0] :]\n\n chosen_logits = all_logits[: batch[\"chosen_input_ids\"].shape[0]]\n rejected_logits = all_logits[batch[\"chosen_input_ids\"].shape[0] :]\n return (chosen_logps, rejected_logps, chosen_logits, rejected_logits)\n\n def get_batch_metrics(\n self,\n model,\n batch: Dict[str, Union[List, torch.LongTensor]],\n train_eval: Literal[\"train\", \"eval\"] = \"train\",\n ):\n \"\"\"Compute the DPO loss and other metrics for the given batch of inputs for train or test.\"\"\"\n metrics = {}\n\n (\n policy_chosen_logps,\n policy_rejected_logps,\n policy_chosen_logits,\n policy_rejected_logits,\n ) = self.concatenated_forward(model, batch)\n with torch.no_grad():\n if self.ref_model is None:\n with self.accelerator.unwrap_model(self.model).disable_adapter():\n (\n reference_chosen_logps,\n reference_rejected_logps,\n _,\n _,\n ) = self.concatenated_forward(self.model, batch)\n else:\n (\n reference_chosen_logps,\n reference_rejected_logps,\n _,\n _,\n ) = self.concatenated_forward(self.ref_model, batch)\n\n losses, chosen_rewards, rejected_rewards = self.dpo_loss(\n policy_chosen_logps,\n policy_rejected_logps,\n reference_chosen_logps,\n reference_rejected_logps,\n )\n reward_accuracies = (chosen_rewards > rejected_rewards).float()\n\n prefix = \"eval_\" if train_eval == \"eval\" else \"\"\n metrics[f\"{prefix}rewards/chosen\"] = chosen_rewards.cpu().numpy().mean()\n metrics[f\"{prefix}rewards/rejected\"] = rejected_rewards.cpu().numpy().mean()\n metrics[f\"{prefix}rewards/accuracies\"] = reward_accuracies.cpu().numpy().mean()\n metrics[f\"{prefix}rewards/margins\"] = (chosen_rewards - rejected_rewards).cpu().numpy().mean()\n metrics[f\"{prefix}logps/rejected\"] = policy_rejected_logps.detach().cpu().numpy().mean()\n metrics[f\"{prefix}logps/chosen\"] = policy_chosen_logps.detach().cpu().numpy().mean()\n metrics[f\"{prefix}logits/rejected\"] = policy_rejected_logits.detach().cpu().numpy().mean()\n metrics[f\"{prefix}logits/chosen\"] = policy_chosen_logits.detach().cpu().numpy().mean()\n\n return losses.mean(), metrics\n\n def compute_loss(\n self,\n model: Union[PreTrainedModel, nn.Module],\n inputs: Dict[str, Union[torch.Tensor, Any]],\n return_outputs=False,\n ) -> Union[torch.Tensor, Tuple[torch.Tensor, Dict[str, torch.Tensor]]]:\n if not self.use_dpo_data_collator:\n warnings.warn(\n \"compute_loss is only implemented for DPODataCollatorWithPadding, and you passed a datacollator that is different than \"\n \"DPODataCollatorWithPadding - you might see unexpected behavior. Alternatively, you can implement your own prediction_step method if you are using a custom data collator\"\n )\n loss, metrics = self.get_batch_metrics(model, inputs, train_eval=\"train\")\n\n # force log the metrics\n if self.accelerator.is_main_process:\n self.store_metrics(metrics, train_eval=\"train\")\n\n if return_outputs:\n return (loss, metrics)\n return loss\n\n def get_batch_samples(self, model, batch: Dict[str, torch.LongTensor]) -> Tuple[str, str]:\n \"\"\"Generate samples from the model and reference model for the given batch of inputs.\"\"\"\n\n policy_output = model.generate(\n batch[\"prompt_input_ids\"],\n attention_mask=batch[\"prompt_attention_mask\"],\n max_length=self.config.max_length,\n do_sample=True,\n pad_token_id=self.tokenizer.pad_token_id,\n )\n\n if self.ref_model is None:\n with self.accelerator.unwrap_model(self.model).disable_adapter():\n reference_output = self.model.generate(\n batch[\"prompt_input_ids\"],\n attention_mask=batch[\"prompt_attention_mask\"],\n max_length=self.config.max_length,\n do_sample=True,\n pad_token_id=self.tokenizer.pad_token_id,\n )\n else:\n reference_output = self.ref_model.generate(\n batch[\"prompt_input_ids\"],\n attention_mask=batch[\"prompt_attention_mask\"],\n max_length=self.config.max_length,\n do_sample=True,\n pad_token_id=self.tokenizer.pad_token_id,\n )\n\n policy_output = pad_to_length(policy_output, self.config.max_length, self.tokenizer.pad_token_id)\n policy_output_decoded = self.tokenizer.batch_decode(policy_output, skip_special_tokens=True)\n\n reference_output = pad_to_length(reference_output, self.config.max_length, self.tokenizer.pad_token_id)\n reference_output_decoded = self.tokenizer.batch_decode(reference_output, skip_special_tokens=True)\n\n return policy_output_decoded, reference_output_decoded\n\n def prediction_step(\n self,\n model: Union[PreTrainedModel, nn.Module],\n inputs: Dict[str, Union[torch.Tensor, Any]],\n prediction_loss_only: bool,\n ignore_keys: Optional[List[str]] = None,\n ):\n if not self.use_dpo_data_collator:\n warnings.warn(\n \"prediction_step is only implemented for DPODataCollatorWithPadding, and you passed a datacollator that is different than \"\n \"DPODataCollatorWithPadding - you might see unexpected behavior. Alternatively, you can implement your own prediction_step method if you are using a custom data collator\"\n )\n if ignore_keys is None:\n if hasattr(model, \"config\"):\n ignore_keys = getattr(model.config, \"keys_to_ignore_at_inference\", [])\n else:\n ignore_keys = []\n\n with torch.no_grad():\n loss, metrics = self.get_batch_metrics(model, inputs, train_eval=\"eval\")\n\n # force log the metrics\n if self.accelerator.is_main_process:\n self.store_metrics(metrics, train_eval=\"eval\")\n\n if prediction_loss_only:\n return (loss.detach(), None, None)\n\n # logits for the chosen and rejected samples from model\n logits_dict = {\n \"logits_test/chosen\": metrics[\"logits_test/chosen\"],\n \"logits_test/rejected\": metrics[\"logits_test/rejected\"],\n }\n logits = tuple(v for k, v in logits_dict.items() if k not in ignore_keys)\n logits = torch.stack(logits).mean(axis=1)\n labels = torch.zeros(logits.shape[0])\n\n return (loss.detach(), logits, labels)\n\n def store_metrics(self, metrics: Dict[str, float], train_eval: Literal[\"train\", \"eval\"] = \"train\") -> None:\n for key, value in metrics.items():\n self._stored_metrics[train_eval][key].append(value)\n\n def log(self, logs: Dict[str, float]) -> None:\n \"\"\"\n Log `logs` on the various objects watching training, including stored metrics.\n\n Args:\n logs (`Dict[str, float]`):\n The values to log.\n \"\"\"\n # logs either has 'loss' or 'eval_loss'\n train_eval = \"train\" if \"loss\" in logs else \"eval\"\n # Add averaged stored metrics to logs\n for key, metrics in self._stored_metrics[train_eval].items():\n logs[key] = torch.tensor(metrics).mean().item()\n del self._stored_metrics[train_eval]\n return super().log(logs)\n","path":"trl/trainer/dpo_trainer.py"}],"string":"[\n {\n \"content\": \"# DPO Authors: Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, and Chelsea Finn 2023\\n# Copyright 2023 The HuggingFace Team. All rights reserved.\\n#\\n# Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\\n# you may not use this file except in compliance with the License.\\n# You may obtain a copy of the License at\\n#\\n# http://www.apache.org/licenses/LICENSE-2.0\\n#\\n# Unless required by applicable law or agreed to in writing, software\\n# distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\\n# See the License for the specific language governing permissions and\\n# limitations under the License.\\nimport warnings\\nfrom collections import defaultdict\\nfrom typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union\\n\\nimport torch\\nimport torch.nn as nn\\nimport torch.nn.functional as F\\nfrom datasets import Dataset\\nfrom transformers import DataCollator, PreTrainedModel, PreTrainedTokenizerBase, Trainer, TrainingArguments\\nfrom transformers.trainer_callback import TrainerCallback\\n\\nfrom ..import_utils import is_peft_available\\nfrom ..models import create_reference_model\\nfrom .utils import DPODataCollatorWithPadding, disable_dropout_in_model, pad_to_length\\n\\n\\nif is_peft_available():\\n from peft import PeftModel, get_peft_model, prepare_model_for_int8_training\\n\\n\\nclass DPOTrainer(Trainer):\\n r\\\"\\\"\\\"\\n Initialize DPOTrainer.\\n\\n Args:\\n model (`transformers.PreTrainedModel`):\\n The model to train, preferably an `AutoModelForSequenceClassification`.\\n ref_model (`PreTrainedModelWrapper`):\\n Hugging Face transformer model with a casual language modelling head. Used for implicit reward computation and loss. If no\\n reference model is provided, the trainer will create a reference model with the same architecture as the model to be optimized.\\n beta (`float`, defaults to 0.1):\\n The beta factor in DPO loss. Higher beta means less divergence from the initial policy.\\n args (`transformers.TrainingArguments`):\\n The arguments to use for training.\\n data_collator (`transformers.DataCollator`):\\n The data collator to use for training. If None is specified, the default data collator (`DPODataCollatorWithPadding`) will be used\\n which will pad the sequences to the maximum length of the sequences in the batch, given a dataset of paired sequences.\\n label_pad_token_id (`int`, defaults to `-100`):\\n The label pad token id. This argument is required if you want to use the default data collator.\\n padding_value (`int`, defaults to `0`):\\n The padding value. This argument is required if you want to use the default data collator.\\n truncation_mode (`str`, defaults to `keep_end`):\\n The truncation mode to use, either `keep_end` or `keep_start`. This argument is required if you want to use the default data collator.\\n train_dataset (`datasets.Dataset`):\\n The dataset to use for training.\\n eval_dataset (`datasets.Dataset`):\\n The dataset to use for evaluation.\\n tokenizer (`transformers.PreTrainedTokenizerBase`):\\n The tokenizer to use for training. This argument is required if you want to use the default data collator.\\n model_init (`Callable[[], transformers.PreTrainedModel]`):\\n The model initializer to use for training. If None is specified, the default model initializer will be used.\\n callbacks (`List[transformers.TrainerCallback]`):\\n The callbacks to use for training.\\n optimizers (`Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`):\\n The optimizer and scheduler to use for training.\\n preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`):\\n The function to use to preprocess the logits before computing the metrics.\\n max_length (`int`, defaults to `None`):\\n The maximum length of the sequences in the batch. This argument is required if you want to use the default data collator.\\n max_prompt_length (`int`, defaults to `None`):\\n The maximum length of the prompt. This argument is required if you want to use the default data collator.\\n peft_config (`Dict`, defaults to `None`):\\n The PEFT configuration to use for training. If you pass a PEFT configuration, the model will be wrapped in a PEFT model.\\n disable_dropout (`bool`, defaults to `True`):\\n Whether or not to disable dropouts in `model` and `ref_model`.\\n \\\"\\\"\\\"\\n\\n def __init__(\\n self,\\n model: Union[PreTrainedModel, nn.Module] = None,\\n ref_model: Optional[Union[PreTrainedModel, nn.Module]] = None,\\n beta: float = 0.1,\\n args: TrainingArguments = None,\\n data_collator: Optional[DataCollator] = None,\\n label_pad_token_id: int = -100,\\n padding_value: int = 0,\\n truncation_mode: str = \\\"keep_end\\\",\\n train_dataset: Optional[Dataset] = None,\\n eval_dataset: Optional[Union[Dataset, Dict[str, Dataset]]] = None,\\n tokenizer: Optional[PreTrainedTokenizerBase] = None,\\n model_init: Optional[Callable[[], PreTrainedModel]] = None,\\n callbacks: Optional[List[TrainerCallback]] = None,\\n optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (\\n None,\\n None,\\n ),\\n preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,\\n max_length: Optional[int] = None,\\n max_prompt_length: Optional[int] = None,\\n peft_config: Optional[Dict] = None,\\n disable_dropout: bool = True,\\n ):\\n if not is_peft_available() and peft_config is not None:\\n raise ValueError(\\n \\\"PEFT is not installed and you passed a `peft_config` in the trainer's kwargs, please install it to use the PEFT models\\\"\\n )\\n elif is_peft_available() and peft_config is not None:\\n if getattr(model, \\\"is_loaded_in_8bit\\\", False) or getattr(model, \\\"is_loaded_in_4bit\\\", False):\\n model = prepare_model_for_int8_training(model)\\n model = get_peft_model(model, peft_config)\\n\\n self.is_peft_model = is_peft_available() and isinstance(model, PeftModel)\\n\\n if ref_model:\\n self.ref_model = ref_model\\n elif self.is_peft_model:\\n # The `model` with adapters turned off will be used as the reference model\\n self.ref_model = None\\n else:\\n self.ref_model = create_reference_model(model)\\n\\n if data_collator is None:\\n if tokenizer is None:\\n raise ValueError(\\n \\\"max_length or a tokenizer must be specified when using the default DPODataCollatorWithPadding\\\"\\n )\\n if max_length is None:\\n warnings.warn(\\n \\\"When using DPODataCollatorWithPadding, you should set `max_length` in the DPOTrainer's init\\\"\\n \\\" it will be set to `512` by default, but you should do it yourself in the future.\\\",\\n UserWarning,\\n )\\n max_length = 512\\n if max_prompt_length is None:\\n warnings.warn(\\n \\\"When using DPODataCollatorWithPadding, you should set `max_prompt_length` in the DPOTrainer's init\\\"\\n \\\" it will be set to `128` by default, but you should do it yourself in the future.\\\",\\n UserWarning,\\n )\\n max_prompt_length = 128\\n\\n data_collator = DPODataCollatorWithPadding(\\n tokenizer,\\n max_length=max_length,\\n max_prompt_length=max_prompt_length,\\n label_pad_token_id=label_pad_token_id,\\n padding_value=padding_value,\\n truncation_mode=truncation_mode,\\n )\\n\\n if args.remove_unused_columns:\\n args.remove_unused_columns = False\\n # warn users\\n warnings.warn(\\n \\\"When using DPODataCollatorWithPadding, you should set `remove_unused_columns=False` in your TrainingArguments\\\"\\n \\\" we have set it for you, but you should do it yourself in the future.\\\",\\n UserWarning,\\n )\\n\\n self.use_dpo_data_collator = True\\n else:\\n self.use_dpo_data_collator = False\\n\\n if disable_dropout:\\n disable_dropout_in_model(model)\\n if self.ref_model is not None:\\n disable_dropout_in_model(self.ref_model)\\n\\n self.label_pad_token_id = label_pad_token_id\\n self.padding_value = padding_value\\n\\n self.beta = beta\\n\\n self._stored_metrics = defaultdict(lambda: defaultdict(list))\\n\\n super().__init__(\\n model,\\n args,\\n data_collator,\\n train_dataset,\\n eval_dataset,\\n tokenizer,\\n model_init,\\n None,\\n callbacks,\\n optimizers,\\n preprocess_logits_for_metrics,\\n )\\n\\n if not hasattr(self, \\\"accelerator\\\"):\\n raise AttributeError(\\n \\\"Your `Trainer` does not have an `accelerator` object. Consider upgrading `transformers`.\\\"\\n )\\n\\n if self.ref_model is None:\\n if not hasattr(self.accelerator.unwrap_model(self.model), \\\"disable_adapter\\\"):\\n raise ValueError(\\n \\\"You are using a `peft` version that does not support `disable_adapter`. Please update your `peft` version to the latest version.\\\"\\n )\\n else:\\n if self.is_deepspeed_enabled:\\n # Read more about the issue in https://github.com/huggingface/trl/pull/687\\n self.ref_model = self.accelerator._prepare_deepspeed(self.ref_model)\\n self.ref_model.eval()\\n else:\\n self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True)\\n\\n def concatenated_inputs(self, batch: Dict[str, Union[List, torch.LongTensor]]) -> Dict[str, torch.LongTensor]:\\n \\\"\\\"\\\"Concatenate the chosen and rejected inputs into a single tensor.\\n\\n Args:\\n batch: A batch of data. Must contain the keys 'chosen_input_ids' and 'rejected_input_ids', which are tensors of shape (batch_size, sequence_length).\\n\\n Returns:\\n A dictionary containing the concatenated inputs under the key 'concatenated_input_ids'.\\n \\\"\\\"\\\"\\n max_length = max(batch[\\\"chosen_input_ids\\\"].shape[1], batch[\\\"rejected_input_ids\\\"].shape[1])\\n concatenated_batch = {}\\n for k in batch:\\n if k.startswith(\\\"chosen\\\") and isinstance(batch[k], torch.Tensor):\\n pad_value = self.label_pad_token_id if \\\"labels\\\" in k else self.padding_value\\n concatenated_key = k.replace(\\\"chosen\\\", \\\"concatenated\\\")\\n concatenated_batch[concatenated_key] = pad_to_length(batch[k], max_length, pad_value=pad_value)\\n for k in batch:\\n if k.startswith(\\\"rejected\\\") and isinstance(batch[k], torch.Tensor):\\n pad_value = self.label_pad_token_id if \\\"labels\\\" in k else self.padding_value\\n concatenated_key = k.replace(\\\"rejected\\\", \\\"concatenated\\\")\\n concatenated_batch[concatenated_key] = torch.cat(\\n (\\n concatenated_batch[concatenated_key],\\n pad_to_length(batch[k], max_length, pad_value=pad_value),\\n ),\\n dim=0,\\n ).to(self.accelerator.device)\\n return concatenated_batch\\n\\n def dpo_loss(\\n self,\\n policy_chosen_logps: torch.FloatTensor,\\n policy_rejected_logps: torch.FloatTensor,\\n reference_chosen_logps: torch.FloatTensor,\\n reference_rejected_logps: torch.FloatTensor,\\n reference_free: bool = False,\\n ) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:\\n \\\"\\\"\\\"Compute the DPO loss for a batch of policy and reference model log probabilities.\\n\\n Args:\\n policy_chosen_logps: Log probabilities of the policy model for the chosen responses. Shape: (batch_size,)\\n policy_rejected_logps: Log probabilities of the policy model for the rejected responses. Shape: (batch_size,)\\n reference_chosen_logps: Log probabilities of the reference model for the chosen responses. Shape: (batch_size,)\\n reference_rejected_logps: Log probabilities of the reference model for the rejected responses. Shape: (batch_size,)\\n beta: Temperature parameter for the DPO loss, typically something in the range of 0.1 to 0.5. We ignore the reference model as beta -> 0.\\n reference_free: If True, we ignore the _provided_ reference model and implicitly use a reference model that assigns equal probability to all responses.\\n\\n Returns:\\n A tuple of three tensors: (losses, chosen_rewards, rejected_rewards).\\n The losses tensor contains the DPO loss for each example in the batch.\\n The chosen_rewards and rejected_rewards tensors contain the rewards for the chosen and rejected responses, respectively.\\n \\\"\\\"\\\"\\n pi_logratios = policy_chosen_logps - policy_rejected_logps\\n ref_logratios = reference_chosen_logps - reference_rejected_logps\\n\\n if reference_free:\\n ref_logratios = 0\\n\\n logits = pi_logratios - ref_logratios\\n\\n losses = -F.logsigmoid(self.beta * logits)\\n chosen_rewards = self.beta * (policy_chosen_logps - reference_chosen_logps).detach()\\n rejected_rewards = self.beta * (policy_rejected_logps - reference_rejected_logps).detach()\\n\\n return losses, chosen_rewards, rejected_rewards\\n\\n def _get_batch_logps(\\n self,\\n logits: torch.FloatTensor,\\n labels: torch.LongTensor,\\n average_log_prob: bool = False,\\n ) -> torch.FloatTensor:\\n \\\"\\\"\\\"Compute the log probabilities of the given labels under the given logits.\\n\\n Args:\\n logits: Logits of the model (unnormalized). Shape: (batch_size, sequence_length, vocab_size)\\n labels: Labels for which to compute the log probabilities. Label tokens with a value of label_pad_token_id are ignored. Shape: (batch_size, sequence_length)\\n average_log_prob: If True, return the average log probability per (non-masked) token. Otherwise, return the sum of the log probabilities of the (non-masked) tokens.\\n\\n Returns:\\n A tensor of shape (batch_size,) containing the average/sum log probabilities of the given labels under the given logits.\\n \\\"\\\"\\\"\\n if logits.shape[:-1] != labels.shape:\\n raise ValueError(\\\"Logits (batch and sequence length dim) and labels must have the same shape.\\\")\\n\\n labels = labels[:, 1:].clone()\\n logits = logits[:, :-1, :]\\n loss_mask = labels != self.label_pad_token_id\\n\\n # dummy token; we'll ignore the losses on these tokens later\\n labels[labels == self.label_pad_token_id] = 0\\n\\n per_token_logps = torch.gather(logits.log_softmax(-1), dim=2, index=labels.unsqueeze(2)).squeeze(2)\\n\\n if average_log_prob:\\n return (per_token_logps * loss_mask).sum(-1) / loss_mask.sum(-1)\\n else:\\n return (per_token_logps * loss_mask).sum(-1)\\n\\n def concatenated_forward(\\n self, model: nn.Module, batch: Dict[str, Union[List, torch.LongTensor]]\\n ) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:\\n \\\"\\\"\\\"Run the given model on the given batch of inputs, concatenating the chosen and rejected inputs together.\\n\\n We do this to avoid doing two forward passes, because it's faster for FSDP.\\n \\\"\\\"\\\"\\n concatenated_batch = self.concatenated_inputs(batch)\\n all_logits = model(\\n concatenated_batch[\\\"concatenated_input_ids\\\"],\\n attention_mask=concatenated_batch[\\\"concatenated_attention_mask\\\"],\\n ).logits.to(torch.float32)\\n all_logps = self._get_batch_logps(\\n all_logits,\\n concatenated_batch[\\\"concatenated_labels\\\"],\\n average_log_prob=False,\\n )\\n chosen_logps = all_logps[: batch[\\\"chosen_input_ids\\\"].shape[0]]\\n rejected_logps = all_logps[batch[\\\"chosen_input_ids\\\"].shape[0] :]\\n\\n chosen_logits = all_logits[: batch[\\\"chosen_input_ids\\\"].shape[0]]\\n rejected_logits = all_logits[batch[\\\"chosen_input_ids\\\"].shape[0] :]\\n return (chosen_logps, rejected_logps, chosen_logits, rejected_logits)\\n\\n def get_batch_metrics(\\n self,\\n model,\\n batch: Dict[str, Union[List, torch.LongTensor]],\\n train_eval: Literal[\\\"train\\\", \\\"eval\\\"] = \\\"train\\\",\\n ):\\n \\\"\\\"\\\"Compute the DPO loss and other metrics for the given batch of inputs for train or test.\\\"\\\"\\\"\\n metrics = {}\\n\\n (\\n policy_chosen_logps,\\n policy_rejected_logps,\\n policy_chosen_logits,\\n policy_rejected_logits,\\n ) = self.concatenated_forward(model, batch)\\n with torch.no_grad():\\n if self.ref_model is None:\\n with self.accelerator.unwrap_model(self.model).disable_adapter():\\n (\\n reference_chosen_logps,\\n reference_rejected_logps,\\n _,\\n _,\\n ) = self.concatenated_forward(self.model, batch)\\n else:\\n (\\n reference_chosen_logps,\\n reference_rejected_logps,\\n _,\\n _,\\n ) = self.concatenated_forward(self.ref_model, batch)\\n\\n losses, chosen_rewards, rejected_rewards = self.dpo_loss(\\n policy_chosen_logps,\\n policy_rejected_logps,\\n reference_chosen_logps,\\n reference_rejected_logps,\\n )\\n reward_accuracies = (chosen_rewards > rejected_rewards).float()\\n\\n prefix = \\\"eval_\\\" if train_eval == \\\"eval\\\" else \\\"\\\"\\n metrics[f\\\"{prefix}rewards/chosen\\\"] = chosen_rewards.cpu().numpy().mean()\\n metrics[f\\\"{prefix}rewards/rejected\\\"] = rejected_rewards.cpu().numpy().mean()\\n metrics[f\\\"{prefix}rewards/accuracies\\\"] = reward_accuracies.cpu().numpy().mean()\\n metrics[f\\\"{prefix}rewards/margins\\\"] = (chosen_rewards - rejected_rewards).cpu().numpy().mean()\\n metrics[f\\\"{prefix}logps/rejected\\\"] = policy_rejected_logps.detach().cpu().numpy().mean()\\n metrics[f\\\"{prefix}logps/chosen\\\"] = policy_chosen_logps.detach().cpu().numpy().mean()\\n metrics[f\\\"{prefix}logits/rejected\\\"] = policy_rejected_logits.detach().cpu().numpy().mean()\\n metrics[f\\\"{prefix}logits/chosen\\\"] = policy_chosen_logits.detach().cpu().numpy().mean()\\n\\n return losses.mean(), metrics\\n\\n def compute_loss(\\n self,\\n model: Union[PreTrainedModel, nn.Module],\\n inputs: Dict[str, Union[torch.Tensor, Any]],\\n return_outputs=False,\\n ) -> Union[torch.Tensor, Tuple[torch.Tensor, Dict[str, torch.Tensor]]]:\\n if not self.use_dpo_data_collator:\\n warnings.warn(\\n \\\"compute_loss is only implemented for DPODataCollatorWithPadding, and you passed a datacollator that is different than \\\"\\n \\\"DPODataCollatorWithPadding - you might see unexpected behavior. Alternatively, you can implement your own prediction_step method if you are using a custom data collator\\\"\\n )\\n loss, metrics = self.get_batch_metrics(model, inputs, train_eval=\\\"train\\\")\\n\\n # force log the metrics\\n if self.accelerator.is_main_process:\\n self.store_metrics(metrics, train_eval=\\\"train\\\")\\n\\n if return_outputs:\\n return (loss, metrics)\\n return loss\\n\\n def get_batch_samples(self, model, batch: Dict[str, torch.LongTensor]) -> Tuple[str, str]:\\n \\\"\\\"\\\"Generate samples from the model and reference model for the given batch of inputs.\\\"\\\"\\\"\\n\\n policy_output = model.generate(\\n batch[\\\"prompt_input_ids\\\"],\\n attention_mask=batch[\\\"prompt_attention_mask\\\"],\\n max_length=self.config.max_length,\\n do_sample=True,\\n pad_token_id=self.tokenizer.pad_token_id,\\n )\\n\\n if self.ref_model is None:\\n with self.accelerator.unwrap_model(self.model).disable_adapter():\\n reference_output = self.model.generate(\\n batch[\\\"prompt_input_ids\\\"],\\n attention_mask=batch[\\\"prompt_attention_mask\\\"],\\n max_length=self.config.max_length,\\n do_sample=True,\\n pad_token_id=self.tokenizer.pad_token_id,\\n )\\n else:\\n reference_output = self.ref_model.generate(\\n batch[\\\"prompt_input_ids\\\"],\\n attention_mask=batch[\\\"prompt_attention_mask\\\"],\\n max_length=self.config.max_length,\\n do_sample=True,\\n pad_token_id=self.tokenizer.pad_token_id,\\n )\\n\\n policy_output = pad_to_length(policy_output, self.config.max_length, self.tokenizer.pad_token_id)\\n policy_output_decoded = self.tokenizer.batch_decode(policy_output, skip_special_tokens=True)\\n\\n reference_output = pad_to_length(reference_output, self.config.max_length, self.tokenizer.pad_token_id)\\n reference_output_decoded = self.tokenizer.batch_decode(reference_output, skip_special_tokens=True)\\n\\n return policy_output_decoded, reference_output_decoded\\n\\n def prediction_step(\\n self,\\n model: Union[PreTrainedModel, nn.Module],\\n inputs: Dict[str, Union[torch.Tensor, Any]],\\n prediction_loss_only: bool,\\n ignore_keys: Optional[List[str]] = None,\\n ):\\n if not self.use_dpo_data_collator:\\n warnings.warn(\\n \\\"prediction_step is only implemented for DPODataCollatorWithPadding, and you passed a datacollator that is different than \\\"\\n \\\"DPODataCollatorWithPadding - you might see unexpected behavior. Alternatively, you can implement your own prediction_step method if you are using a custom data collator\\\"\\n )\\n if ignore_keys is None:\\n if hasattr(model, \\\"config\\\"):\\n ignore_keys = getattr(model.config, \\\"keys_to_ignore_at_inference\\\", [])\\n else:\\n ignore_keys = []\\n\\n with torch.no_grad():\\n loss, metrics = self.get_batch_metrics(model, inputs, train_eval=\\\"eval\\\")\\n\\n # force log the metrics\\n if self.accelerator.is_main_process:\\n self.store_metrics(metrics, train_eval=\\\"eval\\\")\\n\\n if prediction_loss_only:\\n return (loss.detach(), None, None)\\n\\n # logits for the chosen and rejected samples from model\\n logits_dict = {\\n \\\"logits_test/chosen\\\": metrics[\\\"logits_test/chosen\\\"],\\n \\\"logits_test/rejected\\\": metrics[\\\"logits_test/rejected\\\"],\\n }\\n logits = tuple(v for k, v in logits_dict.items() if k not in ignore_keys)\\n logits = torch.stack(logits).mean(axis=1)\\n labels = torch.zeros(logits.shape[0])\\n\\n return (loss.detach(), logits, labels)\\n\\n def store_metrics(self, metrics: Dict[str, float], train_eval: Literal[\\\"train\\\", \\\"eval\\\"] = \\\"train\\\") -> None:\\n for key, value in metrics.items():\\n self._stored_metrics[train_eval][key].append(value)\\n\\n def log(self, logs: Dict[str, float]) -> None:\\n \\\"\\\"\\\"\\n Log `logs` on the various objects watching training, including stored metrics.\\n\\n Args:\\n logs (`Dict[str, float]`):\\n The values to log.\\n \\\"\\\"\\\"\\n # logs either has 'loss' or 'eval_loss'\\n train_eval = \\\"train\\\" if \\\"loss\\\" in logs else \\\"eval\\\"\\n # Add averaged stored metrics to logs\\n for key, metrics in self._stored_metrics[train_eval].items():\\n logs[key] = torch.tensor(metrics).mean().item()\\n del self._stored_metrics[train_eval]\\n return super().log(logs)\\n\",\n \"path\": \"trl/trainer/dpo_trainer.py\"\n }\n]","isConversational":false},"after_files":{"kind":"list like","value":[{"content":"# DPO Authors: Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, and Chelsea Finn 2023\n# Copyright 2023 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport warnings\nfrom collections import defaultdict\nfrom typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom datasets import Dataset\nfrom transformers import DataCollator, PreTrainedModel, PreTrainedTokenizerBase, Trainer, TrainingArguments\nfrom transformers.trainer_callback import TrainerCallback\n\nfrom ..import_utils import is_peft_available\nfrom ..models import create_reference_model\nfrom .utils import DPODataCollatorWithPadding, disable_dropout_in_model, pad_to_length\n\n\nif is_peft_available():\n from peft import PeftModel, get_peft_model, prepare_model_for_int8_training\n\n\nclass DPOTrainer(Trainer):\n r\"\"\"\n Initialize DPOTrainer.\n\n Args:\n model (`transformers.PreTrainedModel`):\n The model to train, preferably an `AutoModelForSequenceClassification`.\n ref_model (`PreTrainedModelWrapper`):\n Hugging Face transformer model with a casual language modelling head. Used for implicit reward computation and loss. If no\n reference model is provided, the trainer will create a reference model with the same architecture as the model to be optimized.\n beta (`float`, defaults to 0.1):\n The beta factor in DPO loss. Higher beta means less divergence from the initial policy.\n args (`transformers.TrainingArguments`):\n The arguments to use for training.\n data_collator (`transformers.DataCollator`):\n The data collator to use for training. If None is specified, the default data collator (`DPODataCollatorWithPadding`) will be used\n which will pad the sequences to the maximum length of the sequences in the batch, given a dataset of paired sequences.\n label_pad_token_id (`int`, defaults to `-100`):\n The label pad token id. This argument is required if you want to use the default data collator.\n padding_value (`int`, defaults to `0`):\n The padding value. This argument is required if you want to use the default data collator.\n truncation_mode (`str`, defaults to `keep_end`):\n The truncation mode to use, either `keep_end` or `keep_start`. This argument is required if you want to use the default data collator.\n train_dataset (`datasets.Dataset`):\n The dataset to use for training.\n eval_dataset (`datasets.Dataset`):\n The dataset to use for evaluation.\n tokenizer (`transformers.PreTrainedTokenizerBase`):\n The tokenizer to use for training. This argument is required if you want to use the default data collator.\n model_init (`Callable[[], transformers.PreTrainedModel]`):\n The model initializer to use for training. If None is specified, the default model initializer will be used.\n callbacks (`List[transformers.TrainerCallback]`):\n The callbacks to use for training.\n optimizers (`Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`):\n The optimizer and scheduler to use for training.\n preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`):\n The function to use to preprocess the logits before computing the metrics.\n max_length (`int`, defaults to `None`):\n The maximum length of the sequences in the batch. This argument is required if you want to use the default data collator.\n max_prompt_length (`int`, defaults to `None`):\n The maximum length of the prompt. This argument is required if you want to use the default data collator.\n peft_config (`Dict`, defaults to `None`):\n The PEFT configuration to use for training. If you pass a PEFT configuration, the model will be wrapped in a PEFT model.\n disable_dropout (`bool`, defaults to `True`):\n Whether or not to disable dropouts in `model` and `ref_model`.\n \"\"\"\n\n def __init__(\n self,\n model: Union[PreTrainedModel, nn.Module] = None,\n ref_model: Optional[Union[PreTrainedModel, nn.Module]] = None,\n beta: float = 0.1,\n args: TrainingArguments = None,\n data_collator: Optional[DataCollator] = None,\n label_pad_token_id: int = -100,\n padding_value: int = 0,\n truncation_mode: str = \"keep_end\",\n train_dataset: Optional[Dataset] = None,\n eval_dataset: Optional[Union[Dataset, Dict[str, Dataset]]] = None,\n tokenizer: Optional[PreTrainedTokenizerBase] = None,\n model_init: Optional[Callable[[], PreTrainedModel]] = None,\n callbacks: Optional[List[TrainerCallback]] = None,\n optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (\n None,\n None,\n ),\n preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,\n max_length: Optional[int] = None,\n max_prompt_length: Optional[int] = None,\n peft_config: Optional[Dict] = None,\n disable_dropout: bool = True,\n ):\n if not is_peft_available() and peft_config is not None:\n raise ValueError(\n \"PEFT is not installed and you passed a `peft_config` in the trainer's kwargs, please install it to use the PEFT models\"\n )\n elif is_peft_available() and peft_config is not None:\n if getattr(model, \"is_loaded_in_8bit\", False) or getattr(model, \"is_loaded_in_4bit\", False):\n model = prepare_model_for_int8_training(model)\n model = get_peft_model(model, peft_config)\n\n self.is_peft_model = is_peft_available() and isinstance(model, PeftModel)\n\n if ref_model:\n self.ref_model = ref_model\n elif self.is_peft_model:\n # The `model` with adapters turned off will be used as the reference model\n self.ref_model = None\n else:\n self.ref_model = create_reference_model(model)\n\n if data_collator is None:\n if tokenizer is None:\n raise ValueError(\n \"max_length or a tokenizer must be specified when using the default DPODataCollatorWithPadding\"\n )\n if max_length is None:\n warnings.warn(\n \"When using DPODataCollatorWithPadding, you should set `max_length` in the DPOTrainer's init\"\n \" it will be set to `512` by default, but you should do it yourself in the future.\",\n UserWarning,\n )\n max_length = 512\n if max_prompt_length is None:\n warnings.warn(\n \"When using DPODataCollatorWithPadding, you should set `max_prompt_length` in the DPOTrainer's init\"\n \" it will be set to `128` by default, but you should do it yourself in the future.\",\n UserWarning,\n )\n max_prompt_length = 128\n\n data_collator = DPODataCollatorWithPadding(\n tokenizer,\n max_length=max_length,\n max_prompt_length=max_prompt_length,\n label_pad_token_id=label_pad_token_id,\n padding_value=padding_value,\n truncation_mode=truncation_mode,\n )\n\n if args.remove_unused_columns:\n args.remove_unused_columns = False\n # warn users\n warnings.warn(\n \"When using DPODataCollatorWithPadding, you should set `remove_unused_columns=False` in your TrainingArguments\"\n \" we have set it for you, but you should do it yourself in the future.\",\n UserWarning,\n )\n\n self.use_dpo_data_collator = True\n else:\n self.use_dpo_data_collator = False\n\n if disable_dropout:\n disable_dropout_in_model(model)\n if self.ref_model is not None:\n disable_dropout_in_model(self.ref_model)\n\n self.label_pad_token_id = label_pad_token_id\n self.padding_value = padding_value\n\n self.beta = beta\n\n self._stored_metrics = defaultdict(lambda: defaultdict(list))\n\n super().__init__(\n model,\n args,\n data_collator,\n train_dataset,\n eval_dataset,\n tokenizer,\n model_init,\n None,\n callbacks,\n optimizers,\n preprocess_logits_for_metrics,\n )\n\n if not hasattr(self, \"accelerator\"):\n raise AttributeError(\n \"Your `Trainer` does not have an `accelerator` object. Consider upgrading `transformers`.\"\n )\n\n if self.ref_model is None:\n if not hasattr(self.accelerator.unwrap_model(self.model), \"disable_adapter\"):\n raise ValueError(\n \"You are using a `peft` version that does not support `disable_adapter`. Please update your `peft` version to the latest version.\"\n )\n else:\n if self.is_deepspeed_enabled:\n # Read more about the issue in https://github.com/huggingface/trl/pull/687\n self.ref_model = self.accelerator._prepare_deepspeed(self.ref_model)\n self.ref_model.eval()\n else:\n self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True)\n\n def concatenated_inputs(self, batch: Dict[str, Union[List, torch.LongTensor]]) -> Dict[str, torch.LongTensor]:\n \"\"\"Concatenate the chosen and rejected inputs into a single tensor.\n\n Args:\n batch: A batch of data. Must contain the keys 'chosen_input_ids' and 'rejected_input_ids', which are tensors of shape (batch_size, sequence_length).\n\n Returns:\n A dictionary containing the concatenated inputs under the key 'concatenated_input_ids'.\n \"\"\"\n max_length = max(batch[\"chosen_input_ids\"].shape[1], batch[\"rejected_input_ids\"].shape[1])\n concatenated_batch = {}\n for k in batch:\n if k.startswith(\"chosen\") and isinstance(batch[k], torch.Tensor):\n pad_value = self.label_pad_token_id if \"labels\" in k else self.padding_value\n concatenated_key = k.replace(\"chosen\", \"concatenated\")\n concatenated_batch[concatenated_key] = pad_to_length(batch[k], max_length, pad_value=pad_value)\n for k in batch:\n if k.startswith(\"rejected\") and isinstance(batch[k], torch.Tensor):\n pad_value = self.label_pad_token_id if \"labels\" in k else self.padding_value\n concatenated_key = k.replace(\"rejected\", \"concatenated\")\n concatenated_batch[concatenated_key] = torch.cat(\n (\n concatenated_batch[concatenated_key],\n pad_to_length(batch[k], max_length, pad_value=pad_value),\n ),\n dim=0,\n ).to(self.accelerator.device)\n return concatenated_batch\n\n def dpo_loss(\n self,\n policy_chosen_logps: torch.FloatTensor,\n policy_rejected_logps: torch.FloatTensor,\n reference_chosen_logps: torch.FloatTensor,\n reference_rejected_logps: torch.FloatTensor,\n reference_free: bool = False,\n ) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:\n \"\"\"Compute the DPO loss for a batch of policy and reference model log probabilities.\n\n Args:\n policy_chosen_logps: Log probabilities of the policy model for the chosen responses. Shape: (batch_size,)\n policy_rejected_logps: Log probabilities of the policy model for the rejected responses. Shape: (batch_size,)\n reference_chosen_logps: Log probabilities of the reference model for the chosen responses. Shape: (batch_size,)\n reference_rejected_logps: Log probabilities of the reference model for the rejected responses. Shape: (batch_size,)\n beta: Temperature parameter for the DPO loss, typically something in the range of 0.1 to 0.5. We ignore the reference model as beta -> 0.\n reference_free: If True, we ignore the _provided_ reference model and implicitly use a reference model that assigns equal probability to all responses.\n\n Returns:\n A tuple of three tensors: (losses, chosen_rewards, rejected_rewards).\n The losses tensor contains the DPO loss for each example in the batch.\n The chosen_rewards and rejected_rewards tensors contain the rewards for the chosen and rejected responses, respectively.\n \"\"\"\n pi_logratios = policy_chosen_logps - policy_rejected_logps\n ref_logratios = reference_chosen_logps - reference_rejected_logps\n\n if reference_free:\n ref_logratios = 0\n\n logits = pi_logratios - ref_logratios\n\n losses = -F.logsigmoid(self.beta * logits)\n chosen_rewards = self.beta * (policy_chosen_logps - reference_chosen_logps).detach()\n rejected_rewards = self.beta * (policy_rejected_logps - reference_rejected_logps).detach()\n\n return losses, chosen_rewards, rejected_rewards\n\n def _get_batch_logps(\n self,\n logits: torch.FloatTensor,\n labels: torch.LongTensor,\n average_log_prob: bool = False,\n ) -> torch.FloatTensor:\n \"\"\"Compute the log probabilities of the given labels under the given logits.\n\n Args:\n logits: Logits of the model (unnormalized). Shape: (batch_size, sequence_length, vocab_size)\n labels: Labels for which to compute the log probabilities. Label tokens with a value of label_pad_token_id are ignored. Shape: (batch_size, sequence_length)\n average_log_prob: If True, return the average log probability per (non-masked) token. Otherwise, return the sum of the log probabilities of the (non-masked) tokens.\n\n Returns:\n A tensor of shape (batch_size,) containing the average/sum log probabilities of the given labels under the given logits.\n \"\"\"\n if logits.shape[:-1] != labels.shape:\n raise ValueError(\"Logits (batch and sequence length dim) and labels must have the same shape.\")\n\n labels = labels[:, 1:].clone()\n logits = logits[:, :-1, :]\n loss_mask = labels != self.label_pad_token_id\n\n # dummy token; we'll ignore the losses on these tokens later\n labels[labels == self.label_pad_token_id] = 0\n\n per_token_logps = torch.gather(logits.log_softmax(-1), dim=2, index=labels.unsqueeze(2)).squeeze(2)\n\n if average_log_prob:\n return (per_token_logps * loss_mask).sum(-1) / loss_mask.sum(-1)\n else:\n return (per_token_logps * loss_mask).sum(-1)\n\n def concatenated_forward(\n self, model: nn.Module, batch: Dict[str, Union[List, torch.LongTensor]]\n ) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:\n \"\"\"Run the given model on the given batch of inputs, concatenating the chosen and rejected inputs together.\n\n We do this to avoid doing two forward passes, because it's faster for FSDP.\n \"\"\"\n concatenated_batch = self.concatenated_inputs(batch)\n all_logits = model(\n concatenated_batch[\"concatenated_input_ids\"],\n attention_mask=concatenated_batch[\"concatenated_attention_mask\"],\n ).logits.to(torch.float32)\n all_logps = self._get_batch_logps(\n all_logits,\n concatenated_batch[\"concatenated_labels\"],\n average_log_prob=False,\n )\n chosen_logps = all_logps[: batch[\"chosen_input_ids\"].shape[0]]\n rejected_logps = all_logps[batch[\"chosen_input_ids\"].shape[0] :]\n\n chosen_logits = all_logits[: batch[\"chosen_input_ids\"].shape[0]]\n rejected_logits = all_logits[batch[\"chosen_input_ids\"].shape[0] :]\n return (chosen_logps, rejected_logps, chosen_logits, rejected_logits)\n\n def get_batch_metrics(\n self,\n model,\n batch: Dict[str, Union[List, torch.LongTensor]],\n train_eval: Literal[\"train\", \"eval\"] = \"train\",\n ):\n \"\"\"Compute the DPO loss and other metrics for the given batch of inputs for train or test.\"\"\"\n metrics = {}\n\n (\n policy_chosen_logps,\n policy_rejected_logps,\n policy_chosen_logits,\n policy_rejected_logits,\n ) = self.concatenated_forward(model, batch)\n with torch.no_grad():\n if self.ref_model is None:\n with self.accelerator.unwrap_model(self.model).disable_adapter():\n (\n reference_chosen_logps,\n reference_rejected_logps,\n _,\n _,\n ) = self.concatenated_forward(self.model, batch)\n else:\n (\n reference_chosen_logps,\n reference_rejected_logps,\n _,\n _,\n ) = self.concatenated_forward(self.ref_model, batch)\n\n losses, chosen_rewards, rejected_rewards = self.dpo_loss(\n policy_chosen_logps,\n policy_rejected_logps,\n reference_chosen_logps,\n reference_rejected_logps,\n )\n reward_accuracies = (chosen_rewards > rejected_rewards).float()\n\n prefix = \"eval_\" if train_eval == \"eval\" else \"\"\n metrics[f\"{prefix}rewards/chosen\"] = chosen_rewards.cpu().numpy().mean()\n metrics[f\"{prefix}rewards/rejected\"] = rejected_rewards.cpu().numpy().mean()\n metrics[f\"{prefix}rewards/accuracies\"] = reward_accuracies.cpu().numpy().mean()\n metrics[f\"{prefix}rewards/margins\"] = (chosen_rewards - rejected_rewards).cpu().numpy().mean()\n metrics[f\"{prefix}logps/rejected\"] = policy_rejected_logps.detach().cpu().numpy().mean()\n metrics[f\"{prefix}logps/chosen\"] = policy_chosen_logps.detach().cpu().numpy().mean()\n metrics[f\"{prefix}logits/rejected\"] = policy_rejected_logits.detach().cpu().numpy().mean()\n metrics[f\"{prefix}logits/chosen\"] = policy_chosen_logits.detach().cpu().numpy().mean()\n\n return losses.mean(), metrics\n\n def compute_loss(\n self,\n model: Union[PreTrainedModel, nn.Module],\n inputs: Dict[str, Union[torch.Tensor, Any]],\n return_outputs=False,\n ) -> Union[torch.Tensor, Tuple[torch.Tensor, Dict[str, torch.Tensor]]]:\n if not self.use_dpo_data_collator:\n warnings.warn(\n \"compute_loss is only implemented for DPODataCollatorWithPadding, and you passed a datacollator that is different than \"\n \"DPODataCollatorWithPadding - you might see unexpected behavior. Alternatively, you can implement your own prediction_step method if you are using a custom data collator\"\n )\n loss, metrics = self.get_batch_metrics(model, inputs, train_eval=\"train\")\n\n # force log the metrics\n if self.accelerator.is_main_process:\n self.store_metrics(metrics, train_eval=\"train\")\n\n if return_outputs:\n return (loss, metrics)\n return loss\n\n def get_batch_samples(self, model, batch: Dict[str, torch.LongTensor]) -> Tuple[str, str]:\n \"\"\"Generate samples from the model and reference model for the given batch of inputs.\"\"\"\n\n policy_output = model.generate(\n batch[\"prompt_input_ids\"],\n attention_mask=batch[\"prompt_attention_mask\"],\n max_length=self.config.max_length,\n do_sample=True,\n pad_token_id=self.tokenizer.pad_token_id,\n )\n\n if self.ref_model is None:\n with self.accelerator.unwrap_model(self.model).disable_adapter():\n reference_output = self.model.generate(\n batch[\"prompt_input_ids\"],\n attention_mask=batch[\"prompt_attention_mask\"],\n max_length=self.config.max_length,\n do_sample=True,\n pad_token_id=self.tokenizer.pad_token_id,\n )\n else:\n reference_output = self.ref_model.generate(\n batch[\"prompt_input_ids\"],\n attention_mask=batch[\"prompt_attention_mask\"],\n max_length=self.config.max_length,\n do_sample=True,\n pad_token_id=self.tokenizer.pad_token_id,\n )\n\n policy_output = pad_to_length(policy_output, self.config.max_length, self.tokenizer.pad_token_id)\n policy_output_decoded = self.tokenizer.batch_decode(policy_output, skip_special_tokens=True)\n\n reference_output = pad_to_length(reference_output, self.config.max_length, self.tokenizer.pad_token_id)\n reference_output_decoded = self.tokenizer.batch_decode(reference_output, skip_special_tokens=True)\n\n return policy_output_decoded, reference_output_decoded\n\n def prediction_step(\n self,\n model: Union[PreTrainedModel, nn.Module],\n inputs: Dict[str, Union[torch.Tensor, Any]],\n prediction_loss_only: bool,\n ignore_keys: Optional[List[str]] = None,\n ):\n if not self.use_dpo_data_collator:\n warnings.warn(\n \"prediction_step is only implemented for DPODataCollatorWithPadding, and you passed a datacollator that is different than \"\n \"DPODataCollatorWithPadding - you might see unexpected behavior. Alternatively, you can implement your own prediction_step method if you are using a custom data collator\"\n )\n if ignore_keys is None:\n if hasattr(model, \"config\"):\n ignore_keys = getattr(model.config, \"keys_to_ignore_at_inference\", [])\n else:\n ignore_keys = []\n\n with torch.no_grad():\n loss, metrics = self.get_batch_metrics(model, inputs, train_eval=\"eval\")\n\n # force log the metrics\n if self.accelerator.is_main_process:\n self.store_metrics(metrics, train_eval=\"eval\")\n\n if prediction_loss_only:\n return (loss.detach(), None, None)\n\n # logits for the chosen and rejected samples from model\n logits_dict = {\n \"eval_logits/chosen\": metrics[\"eval_logits/chosen\"],\n \"eval_logits/rejected\": metrics[\"eval_logits/rejected\"],\n }\n logits = tuple(v for k, v in logits_dict.items() if k not in ignore_keys)\n logits = torch.stack(logits).mean(axis=1)\n labels = torch.zeros(logits.shape[0])\n\n return (loss.detach(), logits, labels)\n\n def store_metrics(self, metrics: Dict[str, float], train_eval: Literal[\"train\", \"eval\"] = \"train\") -> None:\n for key, value in metrics.items():\n self._stored_metrics[train_eval][key].append(value)\n\n def log(self, logs: Dict[str, float]) -> None:\n \"\"\"\n Log `logs` on the various objects watching training, including stored metrics.\n\n Args:\n logs (`Dict[str, float]`):\n The values to log.\n \"\"\"\n # logs either has 'loss' or 'eval_loss'\n train_eval = \"train\" if \"loss\" in logs else \"eval\"\n # Add averaged stored metrics to logs\n for key, metrics in self._stored_metrics[train_eval].items():\n logs[key] = torch.tensor(metrics).mean().item()\n del self._stored_metrics[train_eval]\n return super().log(logs)\n","path":"trl/trainer/dpo_trainer.py"}],"string":"[\n {\n \"content\": \"# DPO Authors: Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, and Chelsea Finn 2023\\n# Copyright 2023 The HuggingFace Team. All rights reserved.\\n#\\n# Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\\n# you may not use this file except in compliance with the License.\\n# You may obtain a copy of the License at\\n#\\n# http://www.apache.org/licenses/LICENSE-2.0\\n#\\n# Unless required by applicable law or agreed to in writing, software\\n# distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\\n# See the License for the specific language governing permissions and\\n# limitations under the License.\\nimport warnings\\nfrom collections import defaultdict\\nfrom typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union\\n\\nimport torch\\nimport torch.nn as nn\\nimport torch.nn.functional as F\\nfrom datasets import Dataset\\nfrom transformers import DataCollator, PreTrainedModel, PreTrainedTokenizerBase, Trainer, TrainingArguments\\nfrom transformers.trainer_callback import TrainerCallback\\n\\nfrom ..import_utils import is_peft_available\\nfrom ..models import create_reference_model\\nfrom .utils import DPODataCollatorWithPadding, disable_dropout_in_model, pad_to_length\\n\\n\\nif is_peft_available():\\n from peft import PeftModel, get_peft_model, prepare_model_for_int8_training\\n\\n\\nclass DPOTrainer(Trainer):\\n r\\\"\\\"\\\"\\n Initialize DPOTrainer.\\n\\n Args:\\n model (`transformers.PreTrainedModel`):\\n The model to train, preferably an `AutoModelForSequenceClassification`.\\n ref_model (`PreTrainedModelWrapper`):\\n Hugging Face transformer model with a casual language modelling head. Used for implicit reward computation and loss. If no\\n reference model is provided, the trainer will create a reference model with the same architecture as the model to be optimized.\\n beta (`float`, defaults to 0.1):\\n The beta factor in DPO loss. Higher beta means less divergence from the initial policy.\\n args (`transformers.TrainingArguments`):\\n The arguments to use for training.\\n data_collator (`transformers.DataCollator`):\\n The data collator to use for training. If None is specified, the default data collator (`DPODataCollatorWithPadding`) will be used\\n which will pad the sequences to the maximum length of the sequences in the batch, given a dataset of paired sequences.\\n label_pad_token_id (`int`, defaults to `-100`):\\n The label pad token id. This argument is required if you want to use the default data collator.\\n padding_value (`int`, defaults to `0`):\\n The padding value. This argument is required if you want to use the default data collator.\\n truncation_mode (`str`, defaults to `keep_end`):\\n The truncation mode to use, either `keep_end` or `keep_start`. This argument is required if you want to use the default data collator.\\n train_dataset (`datasets.Dataset`):\\n The dataset to use for training.\\n eval_dataset (`datasets.Dataset`):\\n The dataset to use for evaluation.\\n tokenizer (`transformers.PreTrainedTokenizerBase`):\\n The tokenizer to use for training. This argument is required if you want to use the default data collator.\\n model_init (`Callable[[], transformers.PreTrainedModel]`):\\n The model initializer to use for training. If None is specified, the default model initializer will be used.\\n callbacks (`List[transformers.TrainerCallback]`):\\n The callbacks to use for training.\\n optimizers (`Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`):\\n The optimizer and scheduler to use for training.\\n preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`):\\n The function to use to preprocess the logits before computing the metrics.\\n max_length (`int`, defaults to `None`):\\n The maximum length of the sequences in the batch. This argument is required if you want to use the default data collator.\\n max_prompt_length (`int`, defaults to `None`):\\n The maximum length of the prompt. This argument is required if you want to use the default data collator.\\n peft_config (`Dict`, defaults to `None`):\\n The PEFT configuration to use for training. If you pass a PEFT configuration, the model will be wrapped in a PEFT model.\\n disable_dropout (`bool`, defaults to `True`):\\n Whether or not to disable dropouts in `model` and `ref_model`.\\n \\\"\\\"\\\"\\n\\n def __init__(\\n self,\\n model: Union[PreTrainedModel, nn.Module] = None,\\n ref_model: Optional[Union[PreTrainedModel, nn.Module]] = None,\\n beta: float = 0.1,\\n args: TrainingArguments = None,\\n data_collator: Optional[DataCollator] = None,\\n label_pad_token_id: int = -100,\\n padding_value: int = 0,\\n truncation_mode: str = \\\"keep_end\\\",\\n train_dataset: Optional[Dataset] = None,\\n eval_dataset: Optional[Union[Dataset, Dict[str, Dataset]]] = None,\\n tokenizer: Optional[PreTrainedTokenizerBase] = None,\\n model_init: Optional[Callable[[], PreTrainedModel]] = None,\\n callbacks: Optional[List[TrainerCallback]] = None,\\n optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (\\n None,\\n None,\\n ),\\n preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,\\n max_length: Optional[int] = None,\\n max_prompt_length: Optional[int] = None,\\n peft_config: Optional[Dict] = None,\\n disable_dropout: bool = True,\\n ):\\n if not is_peft_available() and peft_config is not None:\\n raise ValueError(\\n \\\"PEFT is not installed and you passed a `peft_config` in the trainer's kwargs, please install it to use the PEFT models\\\"\\n )\\n elif is_peft_available() and peft_config is not None:\\n if getattr(model, \\\"is_loaded_in_8bit\\\", False) or getattr(model, \\\"is_loaded_in_4bit\\\", False):\\n model = prepare_model_for_int8_training(model)\\n model = get_peft_model(model, peft_config)\\n\\n self.is_peft_model = is_peft_available() and isinstance(model, PeftModel)\\n\\n if ref_model:\\n self.ref_model = ref_model\\n elif self.is_peft_model:\\n # The `model` with adapters turned off will be used as the reference model\\n self.ref_model = None\\n else:\\n self.ref_model = create_reference_model(model)\\n\\n if data_collator is None:\\n if tokenizer is None:\\n raise ValueError(\\n \\\"max_length or a tokenizer must be specified when using the default DPODataCollatorWithPadding\\\"\\n )\\n if max_length is None:\\n warnings.warn(\\n \\\"When using DPODataCollatorWithPadding, you should set `max_length` in the DPOTrainer's init\\\"\\n \\\" it will be set to `512` by default, but you should do it yourself in the future.\\\",\\n UserWarning,\\n )\\n max_length = 512\\n if max_prompt_length is None:\\n warnings.warn(\\n \\\"When using DPODataCollatorWithPadding, you should set `max_prompt_length` in the DPOTrainer's init\\\"\\n \\\" it will be set to `128` by default, but you should do it yourself in the future.\\\",\\n UserWarning,\\n )\\n max_prompt_length = 128\\n\\n data_collator = DPODataCollatorWithPadding(\\n tokenizer,\\n max_length=max_length,\\n max_prompt_length=max_prompt_length,\\n label_pad_token_id=label_pad_token_id,\\n padding_value=padding_value,\\n truncation_mode=truncation_mode,\\n )\\n\\n if args.remove_unused_columns:\\n args.remove_unused_columns = False\\n # warn users\\n warnings.warn(\\n \\\"When using DPODataCollatorWithPadding, you should set `remove_unused_columns=False` in your TrainingArguments\\\"\\n \\\" we have set it for you, but you should do it yourself in the future.\\\",\\n UserWarning,\\n )\\n\\n self.use_dpo_data_collator = True\\n else:\\n self.use_dpo_data_collator = False\\n\\n if disable_dropout:\\n disable_dropout_in_model(model)\\n if self.ref_model is not None:\\n disable_dropout_in_model(self.ref_model)\\n\\n self.label_pad_token_id = label_pad_token_id\\n self.padding_value = padding_value\\n\\n self.beta = beta\\n\\n self._stored_metrics = defaultdict(lambda: defaultdict(list))\\n\\n super().__init__(\\n model,\\n args,\\n data_collator,\\n train_dataset,\\n eval_dataset,\\n tokenizer,\\n model_init,\\n None,\\n callbacks,\\n optimizers,\\n preprocess_logits_for_metrics,\\n )\\n\\n if not hasattr(self, \\\"accelerator\\\"):\\n raise AttributeError(\\n \\\"Your `Trainer` does not have an `accelerator` object. Consider upgrading `transformers`.\\\"\\n )\\n\\n if self.ref_model is None:\\n if not hasattr(self.accelerator.unwrap_model(self.model), \\\"disable_adapter\\\"):\\n raise ValueError(\\n \\\"You are using a `peft` version that does not support `disable_adapter`. Please update your `peft` version to the latest version.\\\"\\n )\\n else:\\n if self.is_deepspeed_enabled:\\n # Read more about the issue in https://github.com/huggingface/trl/pull/687\\n self.ref_model = self.accelerator._prepare_deepspeed(self.ref_model)\\n self.ref_model.eval()\\n else:\\n self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True)\\n\\n def concatenated_inputs(self, batch: Dict[str, Union[List, torch.LongTensor]]) -> Dict[str, torch.LongTensor]:\\n \\\"\\\"\\\"Concatenate the chosen and rejected inputs into a single tensor.\\n\\n Args:\\n batch: A batch of data. Must contain the keys 'chosen_input_ids' and 'rejected_input_ids', which are tensors of shape (batch_size, sequence_length).\\n\\n Returns:\\n A dictionary containing the concatenated inputs under the key 'concatenated_input_ids'.\\n \\\"\\\"\\\"\\n max_length = max(batch[\\\"chosen_input_ids\\\"].shape[1], batch[\\\"rejected_input_ids\\\"].shape[1])\\n concatenated_batch = {}\\n for k in batch:\\n if k.startswith(\\\"chosen\\\") and isinstance(batch[k], torch.Tensor):\\n pad_value = self.label_pad_token_id if \\\"labels\\\" in k else self.padding_value\\n concatenated_key = k.replace(\\\"chosen\\\", \\\"concatenated\\\")\\n concatenated_batch[concatenated_key] = pad_to_length(batch[k], max_length, pad_value=pad_value)\\n for k in batch:\\n if k.startswith(\\\"rejected\\\") and isinstance(batch[k], torch.Tensor):\\n pad_value = self.label_pad_token_id if \\\"labels\\\" in k else self.padding_value\\n concatenated_key = k.replace(\\\"rejected\\\", \\\"concatenated\\\")\\n concatenated_batch[concatenated_key] = torch.cat(\\n (\\n concatenated_batch[concatenated_key],\\n pad_to_length(batch[k], max_length, pad_value=pad_value),\\n ),\\n dim=0,\\n ).to(self.accelerator.device)\\n return concatenated_batch\\n\\n def dpo_loss(\\n self,\\n policy_chosen_logps: torch.FloatTensor,\\n policy_rejected_logps: torch.FloatTensor,\\n reference_chosen_logps: torch.FloatTensor,\\n reference_rejected_logps: torch.FloatTensor,\\n reference_free: bool = False,\\n ) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:\\n \\\"\\\"\\\"Compute the DPO loss for a batch of policy and reference model log probabilities.\\n\\n Args:\\n policy_chosen_logps: Log probabilities of the policy model for the chosen responses. Shape: (batch_size,)\\n policy_rejected_logps: Log probabilities of the policy model for the rejected responses. Shape: (batch_size,)\\n reference_chosen_logps: Log probabilities of the reference model for the chosen responses. Shape: (batch_size,)\\n reference_rejected_logps: Log probabilities of the reference model for the rejected responses. Shape: (batch_size,)\\n beta: Temperature parameter for the DPO loss, typically something in the range of 0.1 to 0.5. We ignore the reference model as beta -> 0.\\n reference_free: If True, we ignore the _provided_ reference model and implicitly use a reference model that assigns equal probability to all responses.\\n\\n Returns:\\n A tuple of three tensors: (losses, chosen_rewards, rejected_rewards).\\n The losses tensor contains the DPO loss for each example in the batch.\\n The chosen_rewards and rejected_rewards tensors contain the rewards for the chosen and rejected responses, respectively.\\n \\\"\\\"\\\"\\n pi_logratios = policy_chosen_logps - policy_rejected_logps\\n ref_logratios = reference_chosen_logps - reference_rejected_logps\\n\\n if reference_free:\\n ref_logratios = 0\\n\\n logits = pi_logratios - ref_logratios\\n\\n losses = -F.logsigmoid(self.beta * logits)\\n chosen_rewards = self.beta * (policy_chosen_logps - reference_chosen_logps).detach()\\n rejected_rewards = self.beta * (policy_rejected_logps - reference_rejected_logps).detach()\\n\\n return losses, chosen_rewards, rejected_rewards\\n\\n def _get_batch_logps(\\n self,\\n logits: torch.FloatTensor,\\n labels: torch.LongTensor,\\n average_log_prob: bool = False,\\n ) -> torch.FloatTensor:\\n \\\"\\\"\\\"Compute the log probabilities of the given labels under the given logits.\\n\\n Args:\\n logits: Logits of the model (unnormalized). Shape: (batch_size, sequence_length, vocab_size)\\n labels: Labels for which to compute the log probabilities. Label tokens with a value of label_pad_token_id are ignored. Shape: (batch_size, sequence_length)\\n average_log_prob: If True, return the average log probability per (non-masked) token. Otherwise, return the sum of the log probabilities of the (non-masked) tokens.\\n\\n Returns:\\n A tensor of shape (batch_size,) containing the average/sum log probabilities of the given labels under the given logits.\\n \\\"\\\"\\\"\\n if logits.shape[:-1] != labels.shape:\\n raise ValueError(\\\"Logits (batch and sequence length dim) and labels must have the same shape.\\\")\\n\\n labels = labels[:, 1:].clone()\\n logits = logits[:, :-1, :]\\n loss_mask = labels != self.label_pad_token_id\\n\\n # dummy token; we'll ignore the losses on these tokens later\\n labels[labels == self.label_pad_token_id] = 0\\n\\n per_token_logps = torch.gather(logits.log_softmax(-1), dim=2, index=labels.unsqueeze(2)).squeeze(2)\\n\\n if average_log_prob:\\n return (per_token_logps * loss_mask).sum(-1) / loss_mask.sum(-1)\\n else:\\n return (per_token_logps * loss_mask).sum(-1)\\n\\n def concatenated_forward(\\n self, model: nn.Module, batch: Dict[str, Union[List, torch.LongTensor]]\\n ) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:\\n \\\"\\\"\\\"Run the given model on the given batch of inputs, concatenating the chosen and rejected inputs together.\\n\\n We do this to avoid doing two forward passes, because it's faster for FSDP.\\n \\\"\\\"\\\"\\n concatenated_batch = self.concatenated_inputs(batch)\\n all_logits = model(\\n concatenated_batch[\\\"concatenated_input_ids\\\"],\\n attention_mask=concatenated_batch[\\\"concatenated_attention_mask\\\"],\\n ).logits.to(torch.float32)\\n all_logps = self._get_batch_logps(\\n all_logits,\\n concatenated_batch[\\\"concatenated_labels\\\"],\\n average_log_prob=False,\\n )\\n chosen_logps = all_logps[: batch[\\\"chosen_input_ids\\\"].shape[0]]\\n rejected_logps = all_logps[batch[\\\"chosen_input_ids\\\"].shape[0] :]\\n\\n chosen_logits = all_logits[: batch[\\\"chosen_input_ids\\\"].shape[0]]\\n rejected_logits = all_logits[batch[\\\"chosen_input_ids\\\"].shape[0] :]\\n return (chosen_logps, rejected_logps, chosen_logits, rejected_logits)\\n\\n def get_batch_metrics(\\n self,\\n model,\\n batch: Dict[str, Union[List, torch.LongTensor]],\\n train_eval: Literal[\\\"train\\\", \\\"eval\\\"] = \\\"train\\\",\\n ):\\n \\\"\\\"\\\"Compute the DPO loss and other metrics for the given batch of inputs for train or test.\\\"\\\"\\\"\\n metrics = {}\\n\\n (\\n policy_chosen_logps,\\n policy_rejected_logps,\\n policy_chosen_logits,\\n policy_rejected_logits,\\n ) = self.concatenated_forward(model, batch)\\n with torch.no_grad():\\n if self.ref_model is None:\\n with self.accelerator.unwrap_model(self.model).disable_adapter():\\n (\\n reference_chosen_logps,\\n reference_rejected_logps,\\n _,\\n _,\\n ) = self.concatenated_forward(self.model, batch)\\n else:\\n (\\n reference_chosen_logps,\\n reference_rejected_logps,\\n _,\\n _,\\n ) = self.concatenated_forward(self.ref_model, batch)\\n\\n losses, chosen_rewards, rejected_rewards = self.dpo_loss(\\n policy_chosen_logps,\\n policy_rejected_logps,\\n reference_chosen_logps,\\n reference_rejected_logps,\\n )\\n reward_accuracies = (chosen_rewards > rejected_rewards).float()\\n\\n prefix = \\\"eval_\\\" if train_eval == \\\"eval\\\" else \\\"\\\"\\n metrics[f\\\"{prefix}rewards/chosen\\\"] = chosen_rewards.cpu().numpy().mean()\\n metrics[f\\\"{prefix}rewards/rejected\\\"] = rejected_rewards.cpu().numpy().mean()\\n metrics[f\\\"{prefix}rewards/accuracies\\\"] = reward_accuracies.cpu().numpy().mean()\\n metrics[f\\\"{prefix}rewards/margins\\\"] = (chosen_rewards - rejected_rewards).cpu().numpy().mean()\\n metrics[f\\\"{prefix}logps/rejected\\\"] = policy_rejected_logps.detach().cpu().numpy().mean()\\n metrics[f\\\"{prefix}logps/chosen\\\"] = policy_chosen_logps.detach().cpu().numpy().mean()\\n metrics[f\\\"{prefix}logits/rejected\\\"] = policy_rejected_logits.detach().cpu().numpy().mean()\\n metrics[f\\\"{prefix}logits/chosen\\\"] = policy_chosen_logits.detach().cpu().numpy().mean()\\n\\n return losses.mean(), metrics\\n\\n def compute_loss(\\n self,\\n model: Union[PreTrainedModel, nn.Module],\\n inputs: Dict[str, Union[torch.Tensor, Any]],\\n return_outputs=False,\\n ) -> Union[torch.Tensor, Tuple[torch.Tensor, Dict[str, torch.Tensor]]]:\\n if not self.use_dpo_data_collator:\\n warnings.warn(\\n \\\"compute_loss is only implemented for DPODataCollatorWithPadding, and you passed a datacollator that is different than \\\"\\n \\\"DPODataCollatorWithPadding - you might see unexpected behavior. Alternatively, you can implement your own prediction_step method if you are using a custom data collator\\\"\\n )\\n loss, metrics = self.get_batch_metrics(model, inputs, train_eval=\\\"train\\\")\\n\\n # force log the metrics\\n if self.accelerator.is_main_process:\\n self.store_metrics(metrics, train_eval=\\\"train\\\")\\n\\n if return_outputs:\\n return (loss, metrics)\\n return loss\\n\\n def get_batch_samples(self, model, batch: Dict[str, torch.LongTensor]) -> Tuple[str, str]:\\n \\\"\\\"\\\"Generate samples from the model and reference model for the given batch of inputs.\\\"\\\"\\\"\\n\\n policy_output = model.generate(\\n batch[\\\"prompt_input_ids\\\"],\\n attention_mask=batch[\\\"prompt_attention_mask\\\"],\\n max_length=self.config.max_length,\\n do_sample=True,\\n pad_token_id=self.tokenizer.pad_token_id,\\n )\\n\\n if self.ref_model is None:\\n with self.accelerator.unwrap_model(self.model).disable_adapter():\\n reference_output = self.model.generate(\\n batch[\\\"prompt_input_ids\\\"],\\n attention_mask=batch[\\\"prompt_attention_mask\\\"],\\n max_length=self.config.max_length,\\n do_sample=True,\\n pad_token_id=self.tokenizer.pad_token_id,\\n )\\n else:\\n reference_output = self.ref_model.generate(\\n batch[\\\"prompt_input_ids\\\"],\\n attention_mask=batch[\\\"prompt_attention_mask\\\"],\\n max_length=self.config.max_length,\\n do_sample=True,\\n pad_token_id=self.tokenizer.pad_token_id,\\n )\\n\\n policy_output = pad_to_length(policy_output, self.config.max_length, self.tokenizer.pad_token_id)\\n policy_output_decoded = self.tokenizer.batch_decode(policy_output, skip_special_tokens=True)\\n\\n reference_output = pad_to_length(reference_output, self.config.max_length, self.tokenizer.pad_token_id)\\n reference_output_decoded = self.tokenizer.batch_decode(reference_output, skip_special_tokens=True)\\n\\n return policy_output_decoded, reference_output_decoded\\n\\n def prediction_step(\\n self,\\n model: Union[PreTrainedModel, nn.Module],\\n inputs: Dict[str, Union[torch.Tensor, Any]],\\n prediction_loss_only: bool,\\n ignore_keys: Optional[List[str]] = None,\\n ):\\n if not self.use_dpo_data_collator:\\n warnings.warn(\\n \\\"prediction_step is only implemented for DPODataCollatorWithPadding, and you passed a datacollator that is different than \\\"\\n \\\"DPODataCollatorWithPadding - you might see unexpected behavior. Alternatively, you can implement your own prediction_step method if you are using a custom data collator\\\"\\n )\\n if ignore_keys is None:\\n if hasattr(model, \\\"config\\\"):\\n ignore_keys = getattr(model.config, \\\"keys_to_ignore_at_inference\\\", [])\\n else:\\n ignore_keys = []\\n\\n with torch.no_grad():\\n loss, metrics = self.get_batch_metrics(model, inputs, train_eval=\\\"eval\\\")\\n\\n # force log the metrics\\n if self.accelerator.is_main_process:\\n self.store_metrics(metrics, train_eval=\\\"eval\\\")\\n\\n if prediction_loss_only:\\n return (loss.detach(), None, None)\\n\\n # logits for the chosen and rejected samples from model\\n logits_dict = {\\n \\\"eval_logits/chosen\\\": metrics[\\\"eval_logits/chosen\\\"],\\n \\\"eval_logits/rejected\\\": metrics[\\\"eval_logits/rejected\\\"],\\n }\\n logits = tuple(v for k, v in logits_dict.items() if k not in ignore_keys)\\n logits = torch.stack(logits).mean(axis=1)\\n labels = torch.zeros(logits.shape[0])\\n\\n return (loss.detach(), logits, labels)\\n\\n def store_metrics(self, metrics: Dict[str, float], train_eval: Literal[\\\"train\\\", \\\"eval\\\"] = \\\"train\\\") -> None:\\n for key, value in metrics.items():\\n self._stored_metrics[train_eval][key].append(value)\\n\\n def log(self, logs: Dict[str, float]) -> None:\\n \\\"\\\"\\\"\\n Log `logs` on the various objects watching training, including stored metrics.\\n\\n Args:\\n logs (`Dict[str, float]`):\\n The values to log.\\n \\\"\\\"\\\"\\n # logs either has 'loss' or 'eval_loss'\\n train_eval = \\\"train\\\" if \\\"loss\\\" in logs else \\\"eval\\\"\\n # Add averaged stored metrics to logs\\n for key, metrics in self._stored_metrics[train_eval].items():\\n logs[key] = torch.tensor(metrics).mean().item()\\n del self._stored_metrics[train_eval]\\n return super().log(logs)\\n\",\n \"path\": \"trl/trainer/dpo_trainer.py\"\n }\n]","isConversational":false},"pr_diff":{"kind":"string","value":"diff --git a/trl/trainer/dpo_trainer.py b/trl/trainer/dpo_trainer.py\nindex c30e2b6c2e..3f4ee1503b 100644\n--- a/trl/trainer/dpo_trainer.py\n+++ b/trl/trainer/dpo_trainer.py\n@@ -472,8 +472,8 @@ def prediction_step(\n \n # logits for the chosen and rejected samples from model\n logits_dict = {\n- \"logits_test/chosen\": metrics[\"logits_test/chosen\"],\n- \"logits_test/rejected\": metrics[\"logits_test/rejected\"],\n+ \"eval_logits/chosen\": metrics[\"eval_logits/chosen\"],\n+ \"eval_logits/rejected\": metrics[\"eval_logits/rejected\"],\n }\n logits = tuple(v for k, v in logits_dict.items() if k not in ignore_keys)\n logits = torch.stack(logits).mean(axis=1)\n"}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":105,"numItemsPerPage":100,"numTotalItems":69711,"offset":10500,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sInJlcG9JZHMiOlsiNjg0NmRhMzM3MjA0OTNlYWQ1ZGVlOTBkIl0sImlhdCI6MTc5MzUwMTI4NSwianRpIjoiNmRkNDAwZGUtNDdmZS00NGNmLTgxMTMtYTg4MmY0YmVjNDk1Iiwic3ViIjoiL2RhdGFzZXRzL3Jhc2RhbmkvZ2l0aHViLXBhdGNoZXMiLCJleHAiOjE3OTM1MDQ4ODUsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.PB-gXjYZ-eiRxzhsaUJl-esPBTMIv-5bGyXYu4TQDn0k9zi0b3Yi0K6i1frRLx-lzbpTX3vC3lr3mxtCdhLvCw","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":69711,"numBytesParquet":2677201019}]},"discussionsStats":{"closed":0,"open":1,"total":1},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
in_source_id stringlengths 13 58 | issue stringlengths 3 241k | before_files listlengths 0 3 | after_files listlengths 0 3 | pr_diff stringlengths 109 107M ⌀ |
|---|---|---|---|---|
comic__grand-challenge.org-1433 | Helptext for supported file formats is not up-to-date
| [
{
"content": "from typing import List\n\nfrom crispy_forms.helper import FormHelper\nfrom crispy_forms.layout import Submit\nfrom django import forms\nfrom django.conf import settings\nfrom django.core.exceptions import ValidationError\n\nfrom grandchallenge.cases.models import RawImageFile, RawImageUploadSessi... | [
{
"content": "from typing import List\n\nfrom crispy_forms.helper import FormHelper\nfrom crispy_forms.layout import Submit\nfrom django import forms\nfrom django.conf import settings\nfrom django.core.exceptions import ValidationError\n\nfrom grandchallenge.cases.models import RawImageFile, RawImageUploadSessi... | diff --git a/app/grandchallenge/cases/forms.py b/app/grandchallenge/cases/forms.py
index a581622cf5..a4f1387e53 100644
--- a/app/grandchallenge/cases/forms.py
+++ b/app/grandchallenge/cases/forms.py
@@ -22,7 +22,10 @@ class UploadRawImagesForm(forms.ModelForm):
"The total size of all files uploaded in a si... |
microsoft__Qcodes-1735 | get_shaped_data_by_runid does not reshape data
### Steps to reproduce
1. use function with qc_run_id corresponding to a 2D dataset
2. print shapes of array and see they are all 1D
### Expected behaviour
Expecting 2D np arrays to be returned
### Potential fix
datatype_from_setpoints_2d seem to return datatypes... | [
{
"content": "from typing import List, Any, Sequence, Tuple, Dict, Union\nimport logging\n\nimport numpy as np\n\nfrom qcodes.dataset.descriptions.param_spec import ParamSpecBase\nfrom qcodes.dataset.sqlite.queries import (get_dependencies, get_dependents,\n get_layout)... | [
{
"content": "from typing import List, Any, Sequence, Tuple, Dict, Union\nimport logging\n\nimport numpy as np\n\nfrom qcodes.dataset.descriptions.param_spec import ParamSpecBase\nfrom qcodes.dataset.sqlite.queries import (get_dependencies, get_dependents,\n get_layout)... | diff --git a/qcodes/dataset/data_export.py b/qcodes/dataset/data_export.py
index 1b7f70a6130..737319656dc 100644
--- a/qcodes/dataset/data_export.py
+++ b/qcodes/dataset/data_export.py
@@ -424,7 +424,7 @@ def get_shaped_data_by_runid(run_id: int) -> List:
datatype = datatype_from_setpoints_2d(independet[... |
nipy__nipype-2828 | Incompatibility with networkx>=2 and version conflict with prov dependency
### Summary
As far as I can tell nipype isn't compatible with networkx>=2, as running with networkx>=2 raises a "graph change during iteration" error. So perhaps this should be reflected in the install requires of the setup.py (i.e. frozen at... | [
{
"content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n\"\"\"Defines functionality for pipelined execution of interfaces\n\nThe `Workflow` class provides core functionality for batch proces... | [
{
"content": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n\"\"\"Defines functionality for pipelined execution of interfaces\n\nThe `Workflow` class provides core functionality for batch proces... | diff --git a/nipype/pipeline/engine/workflows.py b/nipype/pipeline/engine/workflows.py
index 8a3649660f..6c0757bead 100644
--- a/nipype/pipeline/engine/workflows.py
+++ b/nipype/pipeline/engine/workflows.py
@@ -878,7 +878,7 @@ def _generate_flatgraph(self):
if not nx.is_directed_acyclic_graph(self._graph):
... |
fedora-infra__bodhi-2461 | bugs can't be marked as fixed
In https://bodhi.fedoraproject.org/updates/FEDORA-2018-1a6e6196b9 I tried to mark #1580530 as fixed by giving it +1 feedback (directly for that bug). But only generic karma was shown in my [comment](https://bodhi.fedoraproject.org/updates/ansible-2.5.5-2.fc28#comment-800652), not the speci... | [
{
"content": "# -*- coding: utf-8 -*-\n# Copyright © 2013-2017 Red Hat, Inc. and others.\n#\n# This file is part of Bodhi.\n#\n# This program is free software; you can redistribute it and/or\n# modify it under the terms of the GNU General Public License\n# as published by the Free Software Foundation; either ve... | [
{
"content": "# -*- coding: utf-8 -*-\n# Copyright © 2013-2017 Red Hat, Inc. and others.\n#\n# This file is part of Bodhi.\n#\n# This program is free software; you can redistribute it and/or\n# modify it under the terms of the GNU General Public License\n# as published by the Free Software Foundation; either ve... | diff --git a/bodhi/server/schemas.py b/bodhi/server/schemas.py
index 33335de382..590c6480e9 100644
--- a/bodhi/server/schemas.py
+++ b/bodhi/server/schemas.py
@@ -145,6 +145,11 @@ class TestcaseFeedbacks(colander.SequenceSchema):
class SaveCommentSchema(CSRFProtectedSchema, colander.MappingSchema):
"""An API sche... |
bentoml__BentoML-2711 | `--reload` is not supported on Windows
**Describe the bug**
<!--- A clear and concise description of what the bug is. -->
In QuickStart guide, calling `bentoml serve --reload` will throw error:
```
NotImplementedError: Streams are not supported on Windows.
```
**To Reproduce**
<!--
Steps to reproduce the issue:
1. G... | [
{
"content": "from __future__ import annotations\n\nimport os\nimport sys\nimport json\nimport math\nimport shutil\nimport typing as t\nimport logging\nimport tempfile\nimport contextlib\n\nimport psutil\nfrom simple_di import inject\nfrom simple_di import Provide\n\nfrom bentoml import load\n\nfrom ..utils imp... | [
{
"content": "from __future__ import annotations\n\nimport os\nimport sys\nimport json\nimport math\nimport shutil\nimport typing as t\nimport logging\nimport tempfile\nimport contextlib\n\nimport psutil\nfrom simple_di import inject\nfrom simple_di import Provide\n\nfrom bentoml import load\n\nfrom ..utils imp... | diff --git a/bentoml/_internal/server/__init__.py b/bentoml/_internal/server/__init__.py
index 9ea111a9af6..ffa602f94b7 100644
--- a/bentoml/_internal/server/__init__.py
+++ b/bentoml/_internal/server/__init__.py
@@ -106,12 +106,16 @@ def serve_development(
"bentoml_home": bentoml_home,
},... |
pymedusa__Medusa-10257 | uTServer handler - Incorrect labeling with special characters
**Describe the bug**
I am using uTorrent Server (uTServer) Labels for file destinations as it's intended but when the show is named with special characters such as question mark or a colon these characters are forwarded to Labels and then uTServer is unable... | [
{
"content": "# coding=utf-8\n\n\"\"\"uTorrent Client.\"\"\"\n\nfrom __future__ import unicode_literals\n\nimport logging\nimport os\nimport re\nfrom collections import OrderedDict\n\nfrom medusa import app\nfrom medusa.clients.torrent.generic import GenericClient\nfrom medusa.helper.exceptions import DownloadC... | [
{
"content": "# coding=utf-8\n\n\"\"\"uTorrent Client.\"\"\"\n\nfrom __future__ import unicode_literals\n\nimport logging\nimport os\nimport re\nfrom collections import OrderedDict\n\nfrom medusa import app\nfrom medusa.clients.torrent.generic import GenericClient\nfrom medusa.helper.exceptions import DownloadC... | diff --git a/medusa/clients/torrent/utorrent.py b/medusa/clients/torrent/utorrent.py
index 8747c511f8..789a9ee7e5 100644
--- a/medusa/clients/torrent/utorrent.py
+++ b/medusa/clients/torrent/utorrent.py
@@ -133,7 +133,8 @@ def _add_torrent_file(self, result):
def _set_torrent_label(self, result):
"""Sen... |
Azure__azure-cli-extensions-1831 | Error firewall network-rule create when trying to create a rule in a Firewall Manager
### Describe the bug
This happens when trying to add rule to a firewall manager resource:
https://docs.microsoft.com/en-us/azure/firewall-manager/overview
**Command Name**
`az network firewall network-rule create
Extension Na... | [
{
"content": "# --------------------------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License. See License.txt in the project root for license information.\n# -------------------------------------------... | [
{
"content": "# --------------------------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License. See License.txt in the project root for license information.\n# -------------------------------------------... | diff --git a/src/azure-firewall/azext_firewall/custom.py b/src/azure-firewall/azext_firewall/custom.py
index f709ae588ce..58aa7d7e802 100644
--- a/src/azure-firewall/azext_firewall/custom.py
+++ b/src/azure-firewall/azext_firewall/custom.py
@@ -258,7 +258,7 @@ def _upsert_af_rule(cmd, resource_group_name, firewall_name... |
saulpw__visidata-1365 | vdj variable name issues
Hello,
I'm using `visidata` version 4f9a1d5e74f6801e3f17bd8c53c734cef4a24150 and I've created a very simple vdj file with a variable in place, but I'm getting an error on playback.
```
$ cat test.vdj
#!vd -p
{"longname": "open-file", "input": "myfile_{date}.xlsx", "keystrokes": "o"}
`... | [
{
"content": "#\n# Usage: $0 [<options>] [<input> ...]\n# $0 [<options>] --play <cmdlog> [--batch] [-w <waitsecs>] [-o <output>] [field=value ...]\n\n__version__ = '2.9dev'\n__version_info__ = 'saul.pw/VisiData v' + __version__\n\nfrom copy import copy\nimport os\nimport io\nimport sys\nimport locale\nim... | [
{
"content": "#\n# Usage: $0 [<options>] [<input> ...]\n# $0 [<options>] --play <cmdlog> [--batch] [-w <waitsecs>] [-o <output>] [field=value ...]\n\n__version__ = '2.9dev'\n__version_info__ = 'saul.pw/VisiData v' + __version__\n\nfrom copy import copy\nimport os\nimport io\nimport sys\nimport locale\nim... | diff --git a/visidata/main.py b/visidata/main.py
index 1a133e293..04f438c69 100755
--- a/visidata/main.py
+++ b/visidata/main.py
@@ -33,7 +33,11 @@ def eval_vd(logpath, *args, **kwargs):
'Instantiate logpath with args/kwargs replaced and replay all commands.'
log = logpath.read_text()
if args or kwargs:
... |
nautobot__nautobot-877 | Migrate user, social auth, and system settings from Django Admin to be first-class citizens in UI/API
### Proposed Functionality
Before the launch of Nautobot, there was significant work to migrate functionality from Django Admin into core and create first-class views and APIs for webhooks, custom links, and export... | [
{
"content": "from django.conf import settings\nfrom django.contrib.admin import site as admin_site\nfrom taggit.models import Tag\n\n\n# Override default AdminSite attributes so we can avoid creating and\n# registering our own class\nadmin_site.site_header = \"Nautobot Administration\"\nadmin_site.site_title =... | [
{
"content": "from django.conf import settings\nfrom django.contrib.admin import site as admin_site\nfrom social_django.models import Association, Nonce, UserSocialAuth\nfrom taggit.models import Tag\n\n\n# Override default AdminSite attributes so we can avoid creating and\n# registering our own class\nadmin_si... | diff --git a/nautobot/core/admin.py b/nautobot/core/admin.py
index a1e7a224b21..4b572e5545e 100644
--- a/nautobot/core/admin.py
+++ b/nautobot/core/admin.py
@@ -1,5 +1,6 @@
from django.conf import settings
from django.contrib.admin import site as admin_site
+from social_django.models import Association, Nonce, UserSo... |
joke2k__faker-121 | Tests fail (on Xubuntu 14.04) due to timestamp out of range issue
Running the tests fail on my Xubuntu 14.04 virtual machine (32-bit with Python 2.7.6) due to a `ValueError: timestamp out of range for platform time_t` in L246 of `faker/providers/date_time.py`; see below for the output:
```
$ python setup.py test
runni... | [
{
"content": "from __future__ import unicode_literals\nfrom . import BaseProvider\nimport random\nimport re\nfrom time import time, mktime\nfrom datetime import timedelta\nfrom faker.utils.datetime_safe import date, datetime, real_date, real_datetime\nfrom faker.utils import is_string\n\n\ndef datetime_to_times... | [
{
"content": "from __future__ import unicode_literals\nfrom . import BaseProvider\nimport random\nimport re\nfrom time import time, mktime\nfrom datetime import timedelta\nfrom faker.utils.datetime_safe import date, datetime, real_date, real_datetime\nfrom faker.utils import is_string\n\n\ndef datetime_to_times... | diff --git a/faker/providers/date_time.py b/faker/providers/date_time.py
index 381c47df5c..576a0bccc9 100644
--- a/faker/providers/date_time.py
+++ b/faker/providers/date_time.py
@@ -243,7 +243,12 @@ def date_time_ad(cls):
:example DateTime('1265-03-22 21:15:52')
:return datetime
"""
- ... |
huggingface__trl-1531 | a bug which leads to "Cuda: Out of memory" in CPOTrainer (cpo_trainer.py), trl 0.8.1 0.8.2, please fix this bug
there is a bug in CPOTrainer. when runing CPOTrainer after runing sevreal steps, the usage of gpu memory increases and it raises the out-of-memory exception. we found that the exception is caused by missing ... | [
{
"content": "# CPO Authors: Haoran Xu, Amr Sharaf, Yunmo Chen, Weiting Tan, Lingfeng Shen, Benjamin Van Durme, Kenton Murray, Young Jin Kim\n# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in... | [
{
"content": "# CPO Authors: Haoran Xu, Amr Sharaf, Yunmo Chen, Weiting Tan, Lingfeng Shen, Benjamin Van Durme, Kenton Murray, Young Jin Kim\n# Copyright 2024 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in... | diff --git a/trl/trainer/cpo_trainer.py b/trl/trainer/cpo_trainer.py
index 8525f0046b..04e2cf2c9b 100644
--- a/trl/trainer/cpo_trainer.py
+++ b/trl/trainer/cpo_trainer.py
@@ -738,7 +738,7 @@ def get_batch_loss_metrics(
metrics[f"{prefix}logps/chosen"] = policy_chosen_logps.detach().mean().cpu()
metric... |
encode__django-rest-framework-7105 | OpenAPI schema generation does not handle callable defaults
## Checklist
- [x] I have verified that that issue exists against the `master` branch of Django REST framework.
- [x] I have searched for similar issues in both open and closed tickets and cannot find a duplicate.
- [x] This is not a usage question. (Thos... | [
{
"content": "import warnings\nfrom operator import attrgetter\nfrom urllib.parse import urljoin\n\nfrom django.core.validators import (\n DecimalValidator, EmailValidator, MaxLengthValidator, MaxValueValidator,\n MinLengthValidator, MinValueValidator, RegexValidator, URLValidator\n)\nfrom django.db impor... | [
{
"content": "import warnings\nfrom operator import attrgetter\nfrom urllib.parse import urljoin\n\nfrom django.core.validators import (\n DecimalValidator, EmailValidator, MaxLengthValidator, MaxValueValidator,\n MinLengthValidator, MinValueValidator, RegexValidator, URLValidator\n)\nfrom django.db impor... | diff --git a/rest_framework/schemas/openapi.py b/rest_framework/schemas/openapi.py
index 134df50434..fe688facc6 100644
--- a/rest_framework/schemas/openapi.py
+++ b/rest_framework/schemas/openapi.py
@@ -393,7 +393,7 @@ def _map_serializer(self, serializer):
schema['writeOnly'] = True
if fi... |
beetbox__beets-4076 | lyrics: Crash when BeautifulSoup is not installed
### Problem
The Lyrics plugin throws an error/crashes when ran without BeautifulSoup installed.
### Terminal output
_I currently can't run a verbose command because Beets is dragging itself through finding lyrics for many many albums._
The following error is dis... | [
{
"content": "# This file is part of beets.\n# Copyright 2016, Adrian Sampson.\n#\n# Permission is hereby granted, free of charge, to any person obtaining\n# a copy of this software and associated documentation files (the\n# \"Software\"), to deal in the Software without restriction, including\n# without limita... | [
{
"content": "# This file is part of beets.\n# Copyright 2016, Adrian Sampson.\n#\n# Permission is hereby granted, free of charge, to any person obtaining\n# a copy of this software and associated documentation files (the\n# \"Software\"), to deal in the Software without restriction, including\n# without limita... | diff --git a/beetsplug/lyrics.py b/beetsplug/lyrics.py
index f13db7ad00..2cb50ca5ef 100644
--- a/beetsplug/lyrics.py
+++ b/beetsplug/lyrics.py
@@ -757,7 +757,7 @@ def __init__(self):
def sanitize_bs_sources(self, sources):
enabled_sources = []
for source in sources:
- if source.REQUIRE... |
searx__searx-1602 | Searchengine Wikipedia causes Internal Error 500 when searching for placenames
When the searchengine Wikipedia is enabled; I get Internal server errors ("TypeError: encoding without a string argument") when searching for placenames (tested with searchterms 'Amsterdam', 'Singapore', 'Eindhoven' and others).
Disabling... | [
{
"content": "import csv\nimport hashlib\nimport hmac\nimport os\nimport re\n\nfrom babel.core import get_global\nfrom babel.dates import format_date\nfrom codecs import getincrementalencoder\nfrom imp import load_source\nfrom numbers import Number\nfrom os.path import splitext, join\nfrom io import open\nfrom ... | [
{
"content": "import csv\nimport hashlib\nimport hmac\nimport os\nimport re\n\nfrom babel.core import get_global\nfrom babel.dates import format_date\nfrom codecs import getincrementalencoder\nfrom imp import load_source\nfrom numbers import Number\nfrom os.path import splitext, join\nfrom io import open\nfrom ... | diff --git a/searx/utils.py b/searx/utils.py
index dfa22c5fc0..6619dd0aee 100644
--- a/searx/utils.py
+++ b/searx/utils.py
@@ -384,10 +384,17 @@ def load_module(filename, module_dir):
def new_hmac(secret_key, url):
+ try:
+ secret_key_bytes = bytes(secret_key, 'utf-8')
+ except TypeError as err:
+ ... |
goauthentik__authentik-887 | Add support for docker secrets
**Is your feature request related to a problem? Please describe.**
The environment variables in the docker images don´t support docker secrets.
I would like to add `_File` after it, and fill in a path. Something like `/run/secrets/*`
From a security standpoint, please add secrets for... | [
{
"content": "\"\"\"authentik core config loader\"\"\"\nimport os\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom glob import glob\nfrom json import dumps\nfrom time import time\nfrom typing import Any\nfrom urllib.parse import urlparse\n\nimport yaml\nfrom django.conf import I... | [
{
"content": "\"\"\"authentik core config loader\"\"\"\nimport os\nfrom collections.abc import Mapping\nfrom contextlib import contextmanager\nfrom glob import glob\nfrom json import dumps\nfrom time import time\nfrom typing import Any\nfrom urllib.parse import urlparse\n\nimport yaml\nfrom django.conf import I... | diff --git a/authentik/lib/config.py b/authentik/lib/config.py
index 9980b5dc8e8e..41f51595e35a 100644
--- a/authentik/lib/config.py
+++ b/authentik/lib/config.py
@@ -88,10 +88,10 @@ def parse_uri(self, value):
value = os.getenv(url.netloc, url.query)
if url.scheme == "file":
try:
- ... |
aio-libs__aiohttp-4813 | Server static handler includes body for HEAD response
🐞 **Describe the bug**
When using `web.static` to serve static content, the server responds to HEAD requests as if they were GET requests i.e. it includes a body with the file content. Apart from wasting bandwidth, this completely breaks pipelined connections beca... | [
{
"content": "import asyncio\nimport mimetypes\nimport os\nimport pathlib\nfrom functools import partial\nfrom typing import ( # noqa\n IO,\n TYPE_CHECKING,\n Any,\n Awaitable,\n Callable,\n List,\n Optional,\n Union,\n cast,\n)\n\nfrom . import hdrs\nfrom .abc import AbstractStreamW... | [
{
"content": "import asyncio\nimport mimetypes\nimport os\nimport pathlib\nfrom functools import partial\nfrom typing import ( # noqa\n IO,\n TYPE_CHECKING,\n Any,\n Awaitable,\n Callable,\n List,\n Optional,\n Union,\n cast,\n)\n\nfrom . import hdrs\nfrom .abc import AbstractStreamW... | diff --git a/CHANGES/4809.bugfix b/CHANGES/4809.bugfix
new file mode 100644
index 00000000000..dd5142fc84b
--- /dev/null
+++ b/CHANGES/4809.bugfix
@@ -0,0 +1 @@
+Fix HEAD requests for static content.
diff --git a/CONTRIBUTORS.txt b/CONTRIBUTORS.txt
index 87088240089..6ea5ea6e860 100644
--- a/CONTRIBUTORS.txt
+++ b/CONT... |
getpelican__pelican-1967 | Reload settings file when not specified
Explicitly defining a settings file will be reloaded when using the --autoreload option. Using the default will indicate that the settings file is tracked, but will not reload the file when it is changed.
| [
{
"content": "# -*- coding: utf-8 -*-\nfrom __future__ import print_function, unicode_literals\n\nimport argparse\nimport collections\nimport locale\nimport logging\nimport os\nimport re\nimport sys\nimport time\n\nimport six\n\n# pelican.log has to be the first pelican module to be loaded\n# because logging.se... | [
{
"content": "# -*- coding: utf-8 -*-\nfrom __future__ import print_function, unicode_literals\n\nimport argparse\nimport collections\nimport locale\nimport logging\nimport os\nimport re\nimport sys\nimport time\n\nimport six\n\n# pelican.log has to be the first pelican module to be loaded\n# because logging.se... | diff --git a/pelican/__init__.py b/pelican/__init__.py
index 30b3c2f87..f676c33f5 100644
--- a/pelican/__init__.py
+++ b/pelican/__init__.py
@@ -277,6 +277,7 @@ def parse_arguments():
'"output" in the current path.')
parser.add_argument('-s', '--settings', dest='settings',
+ ... |
freqtrade__freqtrade-4932 | current_profit in custom_sell and custom_stoploss does not align with backtest sell value
Since we discussed this in Discord I am skipping the usual bits of information and just opening this for tracking purposes.
Currently the "current_rate" and "current_profit" parameters fed into the custom_sell and custom_stoplo... | [
{
"content": "\"\"\"\nIStrategy interface\nThis module defines the interface to apply for strategies\n\"\"\"\nimport logging\nimport warnings\nfrom abc import ABC, abstractmethod\nfrom datetime import datetime, timedelta, timezone\nfrom enum import Enum\nfrom typing import Dict, List, Optional, Tuple, Union\n\n... | [
{
"content": "\"\"\"\nIStrategy interface\nThis module defines the interface to apply for strategies\n\"\"\"\nimport logging\nimport warnings\nfrom abc import ABC, abstractmethod\nfrom datetime import datetime, timedelta, timezone\nfrom enum import Enum\nfrom typing import Dict, List, Optional, Tuple, Union\n\n... | diff --git a/freqtrade/strategy/interface.py b/freqtrade/strategy/interface.py
index 7483abf6d70..e2cde52eb4b 100644
--- a/freqtrade/strategy/interface.py
+++ b/freqtrade/strategy/interface.py
@@ -573,6 +573,10 @@ def should_sell(self, trade: Trade, rate: float, date: datetime, buy: bool,
sell_signal = SellT... |
freqtrade__freqtrade-1801 | Kraken Read timed out
## Step 1: Have you search for this issue before posting it?
After running the bot in dry-run mode for an hour there was a fatal exception due to a read timeout with using the Kraken api.
## Step 2: Describe your environment
* Python Version: 3.7.3 (`python -V`)
* CCXT version: 1.18.... | [
{
"content": "# pragma pylint: disable=W0603\n\"\"\"\nCryptocurrency Exchanges support\n\"\"\"\nimport logging\nimport inspect\nfrom random import randint\nfrom typing import List, Dict, Tuple, Any, Optional\nfrom datetime import datetime\nfrom math import floor, ceil\n\nimport arrow\nimport asyncio\nimport ccx... | [
{
"content": "# pragma pylint: disable=W0603\n\"\"\"\nCryptocurrency Exchanges support\n\"\"\"\nimport logging\nimport inspect\nfrom random import randint\nfrom typing import List, Dict, Tuple, Any, Optional\nfrom datetime import datetime\nfrom math import floor, ceil\n\nimport arrow\nimport asyncio\nimport ccx... | diff --git a/freqtrade/exchange/exchange.py b/freqtrade/exchange/exchange.py
index 94673fdc344..66857a7a576 100644
--- a/freqtrade/exchange/exchange.py
+++ b/freqtrade/exchange/exchange.py
@@ -222,8 +222,11 @@ def _reload_markets(self) -> None:
> arrow.utcnow().timestamp):
return None
... |
goauthentik__authentik-8938 | Authentik sending email to nobody
**Describe the bug**
Authentik attempted to send email to zero email addresses (To field is blank)
**To Reproduce**
These aren't 100% confirmed but this is what my situation was:
1. Turn off worker
2. Capture oauth authorization URL and change the redirect URL to something inv... | [
{
"content": "\"\"\"authentik events models\"\"\"\n\nimport time\nfrom collections import Counter\nfrom datetime import timedelta\nfrom difflib import get_close_matches\nfrom functools import lru_cache\nfrom inspect import currentframe\nfrom smtplib import SMTPException\nfrom uuid import uuid4\n\nfrom django.ap... | [
{
"content": "\"\"\"authentik events models\"\"\"\n\nimport time\nfrom collections import Counter\nfrom datetime import timedelta\nfrom difflib import get_close_matches\nfrom functools import lru_cache\nfrom inspect import currentframe\nfrom smtplib import SMTPException\nfrom uuid import uuid4\n\nfrom django.ap... | diff --git a/authentik/events/models.py b/authentik/events/models.py
index 1a01ea737739..c76931f93d21 100644
--- a/authentik/events/models.py
+++ b/authentik/events/models.py
@@ -452,6 +452,13 @@ def send_webhook_slack(self, notification: "Notification") -> list[str]:
def send_email(self, notification: "Notifica... |
acl-org__acl-anthology-990 | Recaser bug: adding fixed-case inside tex-math markup
`<tex-math><fixed-case>O</fixed-case>(<fixed-case>M</fixed-case>(n^2))</tex-math>` caused the build to fail in #892
| [
{
"content": "#!/usr/bin/env python3\n\n# protect.py <infile> <outfile>\n# looks for file \"truelist\" in current dir\n\n# cd data/xml\n# for i in *xml ; do (cd ../../tools/fixedcase/ ; python3 ./protect.py ../../data/xml/$i /tmp/$i ; echo $i ); done > log\n\n\nimport lxml.etree as ET\nimport os\nimport sys\nim... | [
{
"content": "#!/usr/bin/env python3\n\n# protect.py <infile> <outfile>\n# looks for file \"truelist\" in current dir\n\n# cd data/xml\n# for i in *xml ; do (cd ../../tools/fixedcase/ ; python3 ./protect.py ../../data/xml/$i /tmp/$i ; echo $i ); done > log\n\n\nimport lxml.etree as ET\nimport os\nimport sys\nim... | diff --git a/bin/fixedcase/protect.py b/bin/fixedcase/protect.py
index 949f7b5e74..ebb2aa04ea 100755
--- a/bin/fixedcase/protect.py
+++ b/bin/fixedcase/protect.py
@@ -24,7 +24,7 @@
# recursive helper called by protect
# protect text of "node", including children, and tails of children
def protect_recurse(node, recas... |
OpenNMT__OpenNMT-tf-986 | "Horovod has been shut down" error when training is finished due to early stopping
Hello,
I'm training a Transformer model using OpenNMT-tf 2.29.0 with Horovod on a single node with 4 GPUs, but if the training is finished due to early stopping (in the example below, early stopping kicked in at 600k steps but 1M step... | [
{
"content": "\"\"\"Training related classes and functions.\"\"\"\n\nimport collections\nimport contextlib\nimport itertools\nimport time\n\nimport tensorflow as tf\n\nfrom opennmt.inputters import text_inputter\nfrom opennmt.optimizers import utils as optimizer_util\nfrom opennmt.utils import misc\n\n\nclass T... | [
{
"content": "\"\"\"Training related classes and functions.\"\"\"\n\nimport collections\nimport contextlib\nimport itertools\nimport time\n\nimport tensorflow as tf\n\nfrom opennmt.inputters import text_inputter\nfrom opennmt.optimizers import utils as optimizer_util\nfrom opennmt.utils import misc\n\n\nclass T... | diff --git a/opennmt/training.py b/opennmt/training.py
index f3cb9548d..8b835f863 100644
--- a/opennmt/training.py
+++ b/opennmt/training.py
@@ -346,6 +346,15 @@ def is_master(self):
def num_replicas(self):
return self._hvd.size()
+ def _evaluate(self, evaluator, step, moving_average=None):
+ ... |
bokeh__bokeh-1360 | bokeh.charts.Bar displays overlapping bars
This is pretty minor, but the Bar class makes bar charts with overlapping bars if there are more than 4 values.
The widths are currently hardcoded, but it would be nice if they were adjusted based on the data and/or adjustable through a keyword argument, etc.
https://github.... | [
{
"content": "\"\"\"This is the Bokeh charts interface. It gives you a high level API to build\ncomplex plot is a simple way.\n\nThis is the Bar class which lets you build your Bar charts just passing\nthe arguments to the Chart class and calling the proper functions.\nIt also add a new chained stacked method.\... | [
{
"content": "\"\"\"This is the Bokeh charts interface. It gives you a high level API to build\ncomplex plot is a simple way.\n\nThis is the Bar class which lets you build your Bar charts just passing\nthe arguments to the Chart class and calling the proper functions.\nIt also add a new chained stacked method.\... | diff --git a/bokeh/charts/bar.py b/bokeh/charts/bar.py
index 0438cf31bb9..1f47033da02 100644
--- a/bokeh/charts/bar.py
+++ b/bokeh/charts/bar.py
@@ -174,7 +174,10 @@ def get_data(self, cat, **value):
"""
self.cat = cat
self.width = [0.8] * len(self.cat)
- self.width_cat = [0.2] * len(s... |
OCA__social-1068 | [15.0] mail_tracking: portal tests fail
<!-- Provide a general summary of the issue in the Title above.
Before opening the issue, please check that there isn't another issue addressing the same problem already created -->
## Module
mail_tracking
## Describe the bug
mail_tracking causes TestPortalWizard.test_po... | [
{
"content": "# Copyright 2016 Antonio Espinosa - <antonio.espinosa@tecnativa.com>\n# License AGPL-3.0 or later (http://www.gnu.org/licenses/agpl.html).\n\nimport re\nimport threading\n\nfrom odoo import api, models, tools\n\n\nclass IrMailServer(models.Model):\n _inherit = \"ir.mail_server\"\n\n def _tra... | [
{
"content": "# Copyright 2016 Antonio Espinosa - <antonio.espinosa@tecnativa.com>\n# License AGPL-3.0 or later (http://www.gnu.org/licenses/agpl.html).\n\nimport re\nimport threading\n\nfrom odoo import api, models, tools\n\n\nclass IrMailServer(models.Model):\n _inherit = \"ir.mail_server\"\n\n def _tra... | diff --git a/mail_tracking/models/ir_mail_server.py b/mail_tracking/models/ir_mail_server.py
index 9f6be4d935..7d4ce7552d 100644
--- a/mail_tracking/models/ir_mail_server.py
+++ b/mail_tracking/models/ir_mail_server.py
@@ -45,10 +45,10 @@ def build_email(
if tracking_email_id:
headers = self._trac... |
docker__docker-py-1972 | Build context (.tar) is not prepared properly
Hey,
This morning I've updated to version `3.1.1` however, using this version i'm getting wired error from docker-engine build:
```
ERROR: Error processing tar file(exit status 1): mkdir /foodir/bardir: no such file or directory
```
and the actual building does not s... | [
{
"content": "import os\nimport re\n\nfrom ..constants import IS_WINDOWS_PLATFORM\nfrom fnmatch import fnmatch\nfrom itertools import chain\nfrom .utils import create_archive\n\n\ndef tar(path, exclude=None, dockerfile=None, fileobj=None, gzip=False):\n root = os.path.abspath(path)\n exclude = exclude or ... | [
{
"content": "import os\nimport re\n\nfrom ..constants import IS_WINDOWS_PLATFORM\nfrom fnmatch import fnmatch\nfrom itertools import chain\nfrom .utils import create_archive\n\n\ndef tar(path, exclude=None, dockerfile=None, fileobj=None, gzip=False):\n root = os.path.abspath(path)\n exclude = exclude or ... | diff --git a/docker/utils/build.py b/docker/utils/build.py
index 1622ec356..894b29936 100644
--- a/docker/utils/build.py
+++ b/docker/utils/build.py
@@ -93,7 +93,7 @@ def match(p):
# Whether this file is implicitely included / excluded.
matched = default if hit is None else hit
sub = list(fil... |
scrapy__scrapy-5269 | ItemLoader: support non-TextResponse
At the moment, `ItemLoader(response=response)` fails if `response` is not a `TextResponse` instance.
Passing a binary response can still be useful, though. For example, to allow processors to access the response from their loader context, and hence be able to report the source UR... | [
{
"content": "\"\"\"\nItem Loader\n\nSee documentation in docs/topics/loaders.rst\n\"\"\"\nimport itemloaders\n\nfrom scrapy.item import Item\nfrom scrapy.selector import Selector\n\n\nclass ItemLoader(itemloaders.ItemLoader):\n \"\"\"\n A user-friendly abstraction to populate an :ref:`item <topics-items>... | [
{
"content": "\"\"\"\nItem Loader\n\nSee documentation in docs/topics/loaders.rst\n\"\"\"\nimport itemloaders\n\nfrom scrapy.item import Item\nfrom scrapy.selector import Selector\n\n\nclass ItemLoader(itemloaders.ItemLoader):\n \"\"\"\n A user-friendly abstraction to populate an :ref:`item <topics-items>... | diff --git a/scrapy/loader/__init__.py b/scrapy/loader/__init__.py
index 014951a8e8b..91337b94950 100644
--- a/scrapy/loader/__init__.py
+++ b/scrapy/loader/__init__.py
@@ -83,6 +83,9 @@ class ItemLoader(itemloaders.ItemLoader):
def __init__(self, item=None, selector=None, response=None, parent=None, **context):... |
freqtrade__freqtrade-2100 | Strategy list is space separated
`--strategy-list Strat1,Strat2` produces an error while `--strategy-list Strat1 Strat2` works
It became a space separated list somehow while its description states it's a comma-separated list:
```
--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]
Provide ... | [
{
"content": "\"\"\"\nDefinition of cli arguments used in arguments.py\n\"\"\"\nimport argparse\nimport os\n\nfrom freqtrade import __version__, constants\n\n\ndef check_int_positive(value: str) -> int:\n try:\n uint = int(value)\n if uint <= 0:\n raise ValueError\n except ValueEr... | [
{
"content": "\"\"\"\nDefinition of cli arguments used in arguments.py\n\"\"\"\nimport argparse\nimport os\n\nfrom freqtrade import __version__, constants\n\n\ndef check_int_positive(value: str) -> int:\n try:\n uint = int(value)\n if uint <= 0:\n raise ValueError\n except ValueEr... | diff --git a/docs/backtesting.md b/docs/backtesting.md
index 179bcee15af..7e9f7ff53a3 100644
--- a/docs/backtesting.md
+++ b/docs/backtesting.md
@@ -57,7 +57,15 @@ freqtrade backtesting --datadir freqtrade/tests/testdata-20180101
freqtrade -s TestStrategy backtesting
```
-Where `-s TestStrategy` refers to the class... |
obspy__obspy-2316 | obspy-scan doc does not document depiction of data overlaps
Hi - Just a short suggestion to modify a sentence in the obspy-scan documentation reporting that blue lines/blocks represent data overlaps. (I had to read the code to figure this out.) Suggest
"Start times of traces with available data are marked by cross... | [
{
"content": "#!/usr/bin/env python\n# 2010-01-27 Moritz Beyreuther\n\"\"\"\nScan a directory to determine the data availability.\n\nScan all specified files/directories, determine which time spans are covered\nfor which stations and plot everything in summarized in one overview plot.\nStart times of traces wit... | [
{
"content": "#!/usr/bin/env python\n# 2010-01-27 Moritz Beyreuther\n\"\"\"\nScan a directory to determine the data availability.\n\nScan all specified files/directories, determine which time spans are covered\nfor which stations and plot everything in summarized in one overview plot.\nStart times of traces wit... | diff --git a/obspy/imaging/scripts/scan.py b/obspy/imaging/scripts/scan.py
index a3cbd9921cb..9155c6652f3 100755
--- a/obspy/imaging/scripts/scan.py
+++ b/obspy/imaging/scripts/scan.py
@@ -6,7 +6,7 @@
Scan all specified files/directories, determine which time spans are covered
for which stations and plot everything i... |
celery__celery-3752 | Celery Worker crashing after first task with TypeError: 'NoneType' object is not callable
## Checklist
- [X] I have included the output of ``celery -A proj report`` in the issue.
(if you are not able to do this, then at least specify the Celery
version affected).
```
software -> celery:4.0.0 (latent... | [
{
"content": "\"\"\"The consumers highly-optimized inner loop.\"\"\"\nfrom __future__ import absolute_import, unicode_literals\nimport errno\nimport socket\nfrom celery import bootsteps\nfrom celery.exceptions import WorkerShutdown, WorkerTerminate, WorkerLostError\nfrom celery.utils.log import get_logger\nfrom... | [
{
"content": "\"\"\"The consumers highly-optimized inner loop.\"\"\"\nfrom __future__ import absolute_import, unicode_literals\nimport errno\nimport socket\nfrom celery import bootsteps\nfrom celery.exceptions import WorkerShutdown, WorkerTerminate, WorkerLostError\nfrom celery.utils.log import get_logger\nfrom... | diff --git a/celery/worker/loops.py b/celery/worker/loops.py
index 3bc45993b3a..b97e6a05808 100644
--- a/celery/worker/loops.py
+++ b/celery/worker/loops.py
@@ -44,10 +44,10 @@ def asynloop(obj, connection, consumer, blueprint, hub, qos,
_enable_amqheartbeats(hub.timer, connection, rate=hbrate)
consumer.on_... |
kymatio__kymatio-316 | Unused 2D Psi filters
The generated 2D filterbank includes filters that are not used in the computation.
Let's create a scattering transform:
```
>>> import kymatio
>>> s = kymatio.Scattering2D(J=2, shape=(32, 32))
```
Let's look at the shapes of the generated filters:
```
>>> print("\n".join('{' + ', '.joi... | [
{
"content": "\"\"\"\nAuthors: Eugene Belilovsky, Edouard Oyallon and Sergey Zagoruyko\nAll rights reserved, 2017.\n\"\"\"\n\n__all__ = ['filter_bank']\n\nimport torch\nimport numpy as np\nfrom .utils import fft2\n\n\ndef filter_bank(M, N, J, L=8):\n \"\"\"\n Builds in Fourier the Morlet filters used ... | [
{
"content": "\"\"\"\nAuthors: Eugene Belilovsky, Edouard Oyallon and Sergey Zagoruyko\nAll rights reserved, 2017.\n\"\"\"\n\n__all__ = ['filter_bank']\n\nimport torch\nimport numpy as np\nfrom .utils import fft2\n\n\ndef filter_bank(M, N, J, L=8):\n \"\"\"\n Builds in Fourier the Morlet filters used ... | diff --git a/kymatio/scattering2d/filter_bank.py b/kymatio/scattering2d/filter_bank.py
index 7c2057ab1..60aae8265 100644
--- a/kymatio/scattering2d/filter_bank.py
+++ b/kymatio/scattering2d/filter_bank.py
@@ -46,7 +46,7 @@ def filter_bank(M, N, J, L=8):
(int(L-L/2-1)-theta) * np.pi / L,
... |
docker__docker-py-352 | attach_socket returns a closed socket when TLS is enabled
https://github.com/d11wtq/dockerpty/issues/13 is actually a docker-py bug: a docker-py client communicating over TLS will always return a closed socket from `attach_socket()`. This appears to be because the response object (returned by `self.post`) is [garbage-c... | [
{
"content": "# Copyright 2013 dotCloud inc.\n\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n\n# http://www.apache.org/licenses/LICENSE-2.0\n\n# Unless requir... | [
{
"content": "# Copyright 2013 dotCloud inc.\n\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n\n# http://www.apache.org/licenses/LICENSE-2.0\n\n# Unless requir... | diff --git a/docker/client.py b/docker/client.py
index 1ff43cc4e..0da71b3c3 100644
--- a/docker/client.py
+++ b/docker/client.py
@@ -259,9 +259,11 @@ def _create_websocket_connection(self, url):
def _get_raw_response_socket(self, response):
self._raise_for_status(response)
if six.PY3:
- ... |
certbot__certbot-5992 | HTTP responses logged as hard-to-read bytes repr in Python 3
If you're having trouble using Certbot and aren't sure you've found a bug or
request for a new feature, please first try asking for help at
https://community.letsencrypt.org/. There is a much larger community there of
people familiar with the project who w... | [
{
"content": "\"\"\"ACME client API.\"\"\"\nimport base64\nimport collections\nimport datetime\nfrom email.utils import parsedate_tz\nimport heapq\nimport logging\nimport time\n\nimport six\nfrom six.moves import http_client # pylint: disable=import-error\nimport josepy as jose\nimport OpenSSL\nimport re\nimpo... | [
{
"content": "\"\"\"ACME client API.\"\"\"\nimport base64\nimport collections\nimport datetime\nfrom email.utils import parsedate_tz\nimport heapq\nimport logging\nimport time\n\nimport six\nfrom six.moves import http_client # pylint: disable=import-error\nimport josepy as jose\nimport OpenSSL\nimport re\nimpo... | diff --git a/acme/acme/client.py b/acme/acme/client.py
index 7807f0eceea..5ccc6ba5b8e 100644
--- a/acme/acme/client.py
+++ b/acme/acme/client.py
@@ -1018,7 +1018,7 @@ def _send_request(self, method, url, *args, **kwargs):
if response.headers.get("Content-Type") == DER_CONTENT_TYPE:
debug_content =... |
docker__docker-py-2129 | Specifying `chunk_size` when saving images results in a max chunk of size 32768, with wrap-around
The docstring in `docker.models.images.Image#save` states that 'data will be streamed as it is received. Default: 2 MB'. This default is from `docker.constants#DEFAULT_DATA_CHUNK_SIZE` which is set to 1024 * 2048, or 2MB. ... | [
{
"content": "import itertools\nimport re\n\nimport six\n\nfrom ..api import APIClient\nfrom ..constants import DEFAULT_DATA_CHUNK_SIZE\nfrom ..errors import BuildError, ImageLoadError, InvalidArgument\nfrom ..utils import parse_repository_tag\nfrom ..utils.json_stream import json_stream\nfrom .resource import ... | [
{
"content": "import itertools\nimport re\n\nimport six\n\nfrom ..api import APIClient\nfrom ..constants import DEFAULT_DATA_CHUNK_SIZE\nfrom ..errors import BuildError, ImageLoadError, InvalidArgument\nfrom ..utils import parse_repository_tag\nfrom ..utils.json_stream import json_stream\nfrom .resource import ... | diff --git a/docker/models/images.py b/docker/models/images.py
index 41632c6a3..7d9ab70b5 100644
--- a/docker/models/images.py
+++ b/docker/models/images.py
@@ -64,9 +64,9 @@ def save(self, chunk_size=DEFAULT_DATA_CHUNK_SIZE):
Get a tarball of an image. Similar to the ``docker save`` command.
Args:
... |
mathesar-foundation__mathesar-2529 | Handle 404 pages throughout the app
## Description
* Client side routing: A non existent url results in a blank screen, or has obscure errors.
* Server side routing: A non existent url entered on the browser results in a 404, which returns a django 404 page.
## Expected behavior
* 404s should be clearly presented... | [
{
"content": "from django.conf import settings\nfrom django.contrib.auth.decorators import login_required\nfrom django.shortcuts import render, redirect, get_object_or_404\nfrom rest_framework import status\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\n\nfrom mat... | [
{
"content": "from django.conf import settings\nfrom django.contrib.auth.decorators import login_required\nfrom django.shortcuts import render, redirect, get_object_or_404\nfrom rest_framework import status\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\n\nfrom mat... | diff --git a/config/urls.py b/config/urls.py
index 4e9d954f5f..76d4272b88 100644
--- a/config/urls.py
+++ b/config/urls.py
@@ -24,3 +24,5 @@
path('', include(mathesar_urls)),
path('api-docs/', include('rest_framework.urls')),
]
+
+handler404 = 'mathesar.views.page_not_found_view'
diff --git a/docker-compose.... |
zostera__django-bootstrap5-521 | Pass "horizontal_field_offset_class" to child renderers
The "horizontal_field_offset_class" doesnt pass to child renderers (from form to fields), so it actually does't work as described in documentation.
| [
{
"content": "from django.forms import (\n BaseForm,\n BaseFormSet,\n BoundField,\n CheckboxInput,\n CheckboxSelectMultiple,\n ClearableFileInput,\n MultiWidget,\n RadioSelect,\n Select,\n)\nfrom django.forms.widgets import Input, SelectMultiple, Textarea\nfrom django.utils.html impor... | [
{
"content": "from django.forms import (\n BaseForm,\n BaseFormSet,\n BoundField,\n CheckboxInput,\n CheckboxSelectMultiple,\n ClearableFileInput,\n MultiWidget,\n RadioSelect,\n Select,\n)\nfrom django.forms.widgets import Input, SelectMultiple, Textarea\nfrom django.utils.html impor... | diff --git a/src/django_bootstrap5/renderers.py b/src/django_bootstrap5/renderers.py
index 0787dcd2..0a3f2d95 100644
--- a/src/django_bootstrap5/renderers.py
+++ b/src/django_bootstrap5/renderers.py
@@ -94,6 +94,7 @@ def get_kwargs(self):
"size": self.size,
"horizontal_label_class": self.horiz... |
boto__botocore-852 | Unable to use non-string header values with Signature Version 4
When using S3 Signature Version 4 it's not possible to use non-string values in headers, such as Content-Length (which expects a integer/long). When passing a non-string value the following exception will be raised: `AttributeError: 'int' object has no att... | [
{
"content": "# Copyright 2014 Amazon.com, Inc. or its affiliates. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"). You\n# may not use this file except in compliance with the License. A copy of\n# the License is located at\n#\n# http://aws.amazon.com/apache2.0/\n#\n# ... | [
{
"content": "# Copyright 2014 Amazon.com, Inc. or its affiliates. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"). You\n# may not use this file except in compliance with the License. A copy of\n# the License is located at\n#\n# http://aws.amazon.com/apache2.0/\n#\n# ... | diff --git a/botocore/serialize.py b/botocore/serialize.py
index 4917264cde..641a73bff1 100644
--- a/botocore/serialize.py
+++ b/botocore/serialize.py
@@ -483,7 +483,7 @@ def _partition_parameters(self, partitioned, param_name,
elif location == 'header':
shape = shape_members[param_name]
... |
mkdocs__mkdocs-276 | Multi-page documentaion does not work on Windows
Multi-page documentation did not work on Windows, possibly because of Windows usage of backward slash instead of forward slash for paths.
Should be similar to... | [
{
"content": "# coding: utf-8\n\n\"\"\"\nDeals with generating the site-wide navigation.\n\nThis consists of building a set of interlinked page and header objects.\n\"\"\"\n\nfrom mkdocs import utils\nimport posixpath\nimport os\n\n\ndef filename_to_title(filename):\n \"\"\"\n Automatically generate a def... | [
{
"content": "# coding: utf-8\n\n\"\"\"\nDeals with generating the site-wide navigation.\n\nThis consists of building a set of interlinked page and header objects.\n\"\"\"\n\nfrom mkdocs import utils\nimport posixpath\nimport os\n\n\ndef filename_to_title(filename):\n \"\"\"\n Automatically generate a def... | diff --git a/mkdocs/nav.py b/mkdocs/nav.py
index 3a0cee0a0a..530c5c73d1 100644
--- a/mkdocs/nav.py
+++ b/mkdocs/nav.py
@@ -210,10 +210,11 @@ def _generate_site_navigation(pages_config, url_context, use_directory_urls=True
assert False, msg
if title is None:
- filename = path.split('/'... |
wandb__wandb-953 | Cannot log a slice as config parameter
`wandb --version && python --version && uname`
* Weights and Biases version: 0.8.31
* Python version: 3.8.1
* Operating System: Linux
### Description
I cannot log a slice as config parameter.
### What I Did
```python
import wandb
wandb.init()
wandb.config.updat... | [
{
"content": "from collections import OrderedDict\n\n# six >=1.13.0 not available in all environmnents yet\n#from six.moves.collections_abc import Sequence\ntry:\n from collections.abc import Sequence\nexcept ImportError:\n from collections import Sequence\n\nimport inspect\nimport logging\nimport os\nimp... | [
{
"content": "from collections import OrderedDict\n\n# six >=1.13.0 not available in all environmnents yet\n#from six.moves.collections_abc import Sequence\ntry:\n from collections.abc import Sequence\nexcept ImportError:\n from collections import Sequence\n\nimport inspect\nimport logging\nimport os\nimp... | diff --git a/wandb/wandb_config.py b/wandb/wandb_config.py
index bbe4ebed19c..8ba35eda1aa 100644
--- a/wandb/wandb_config.py
+++ b/wandb/wandb_config.py
@@ -240,6 +240,9 @@ def _sanitize_val(self, val):
for key, value in six.iteritems(val):
converted[key] = self._sanitize_val(value)
... |
Pyomo__pyomo-2923 | DataPortal bug
# bug found by: Maria Marcos Nuñez
```python
from pyomo.environ import *
model = AbstractModel()
# Definición de los diferentes parametros del modelo
model.qv = Param(within = NonNegativeReals)
# Definicion de las variables
model.D = Var(within = NonNegativeReals, bounds =(0.2, 6),initialize =... | [
{
"content": "# ___________________________________________________________________________\n#\n# Pyomo: Python Optimization Modeling Objects\n# Copyright (c) 2008-2022\n# National Technology and Engineering Solutions of Sandia, LLC\n# Under the terms of Contract DE-NA0003525 with National Technology and\n... | [
{
"content": "# ___________________________________________________________________________\n#\n# Pyomo: Python Optimization Modeling Objects\n# Copyright (c) 2008-2022\n# National Technology and Engineering Solutions of Sandia, LLC\n# Under the terms of Contract DE-NA0003525 with National Technology and\n... | diff --git a/pyomo/dataportal/process_data.py b/pyomo/dataportal/process_data.py
index 1b0354454fa..5eb15269e0c 100644
--- a/pyomo/dataportal/process_data.py
+++ b/pyomo/dataportal/process_data.py
@@ -343,7 +343,8 @@ def _process_param(cmd, _model, _data, _default, index=None, param=None, ncolumn
... |
nonebot__nonebot2-2374 | Bug: on_called_api 后处理异常的逻辑问题
### 操作系统
Windows
### Python 版本
3.10.11
### NoneBot 版本
2.1.0
### 适配器
adapter-qqguild 0.3.0
### 协议端
QQ-Guild
### 描述问题
这应该是一个 `on_called_api` 处理逻辑的问题
## 场景
在 [adapter-qqguild](https://github.com/nonebot/adapter-qqguild) 中发送图片之类的消息会引发审核异常 `AuditException`
为使插件层面不受该异常影响而通过 `on... | [
{
"content": "import abc\nimport asyncio\nfrom functools import partial\nfrom typing import TYPE_CHECKING, Any, Set, Union, Optional, Protocol\n\nfrom nonebot.log import logger\nfrom nonebot.config import Config\nfrom nonebot.exception import MockApiException\nfrom nonebot.typing import T_CalledAPIHook, T_Calli... | [
{
"content": "import abc\nimport asyncio\nfrom functools import partial\nfrom typing import TYPE_CHECKING, Any, Set, Union, Optional, Protocol\n\nfrom nonebot.log import logger\nfrom nonebot.config import Config\nfrom nonebot.exception import MockApiException\nfrom nonebot.typing import T_CalledAPIHook, T_Calli... | diff --git a/nonebot/internal/adapter/bot.py b/nonebot/internal/adapter/bot.py
index 8961b89f3fb6..3f827b18884a 100644
--- a/nonebot/internal/adapter/bot.py
+++ b/nonebot/internal/adapter/bot.py
@@ -106,7 +106,10 @@ async def call_api(self, api: str, **data: Any) -> Any:
logger.debug("Running CalledAPI... |
alltheplaces__alltheplaces-2457 | Spider sheetz is broken
During the global build at 2021-06-23-14-42-18, spider **sheetz** failed with **526 features** and **1 errors**.
Here's [the log](https://data.alltheplaces.xyz/runs/2021-06-23-14-42-18/logs/sheetz.txt) and [the output](https://data.alltheplaces.xyz/runs/2021-06-23-14-42-18/output/sheetz.geojson... | [
{
"content": "import json\nimport re\nimport scrapy\nfrom locations.items import GeojsonPointItem\n\n\nclass SheetzSpider(scrapy.Spider):\n name = \"sheetz\"\n item_attributes = {'brand': \"Sheetz\"}\n allowed_domains = [\"orderz.sheetz.com\"]\n start_urls = (\n \"https://orderz.sheetz.com/sa... | [
{
"content": "import json\nimport re\nimport scrapy\nfrom locations.items import GeojsonPointItem\n\n\nclass SheetzSpider(scrapy.Spider):\n name = \"sheetz\"\n item_attributes = {'brand': \"Sheetz\"}\n allowed_domains = [\"orderz.sheetz.com\"]\n start_urls = (\n \"https://orderz.sheetz.com/sa... | diff --git a/locations/spiders/sheetz.py b/locations/spiders/sheetz.py
index 464c7b6600e..8b969200ca5 100644
--- a/locations/spiders/sheetz.py
+++ b/locations/spiders/sheetz.py
@@ -22,7 +22,7 @@ def parse(self, response):
'state': store['state'],
'postcode': store['zip'],
... |
django-oscar__django-oscar-1770 | Basket View Error with Weight Based shipping and discount Applied
When a discount it's applied to shipping and the shipping method is WeightBased occurs the following error:
File "/Users/christian/virtualenvs/webstores/lib/python2.7/site-packages/django/core/handlers/base.py", line 112, in get_response
response... | [
{
"content": "import json\n\nfrom django import shortcuts\nfrom django.contrib import messages\nfrom django.shortcuts import redirect\nfrom django.template.loader import render_to_string\nfrom django.template import RequestContext\nfrom django.core.urlresolvers import reverse\nfrom django.http import HttpRespon... | [
{
"content": "import json\n\nfrom django import shortcuts\nfrom django.contrib import messages\nfrom django.shortcuts import redirect\nfrom django.template.loader import render_to_string\nfrom django.template import RequestContext\nfrom django.core.urlresolvers import reverse\nfrom django.http import HttpRespon... | diff --git a/src/oscar/apps/basket/views.py b/src/oscar/apps/basket/views.py
index ef827236fd7..95b5b77b41c 100644
--- a/src/oscar/apps/basket/views.py
+++ b/src/oscar/apps/basket/views.py
@@ -159,7 +159,7 @@ def get_context_data(self, **kwargs):
shipping_charge = method.calculate(self.request.basket)
... |
Cog-Creators__Red-DiscordBot-2722 | [Help] No help without a docstring
# Other bugs
#### What were you trying to do?
`[p]help <command>`
alternatively, `[p]<command>` with missing required arguments.
#### What were you expecting to happen?
The help embed to show.
#### What actually happened?
Nothing, if the docstring for the command do... | [
{
"content": "# This is a full replacement of discord.py's help command\n# Signatures are not guaranteed to be unchanging in this file.\n# At a later date when this is more set in stone, this warning will be removed.\n# At said later date, there should also be things added to support extra formatter\n# registra... | [
{
"content": "# This is a full replacement of discord.py's help command\n# Signatures are not guaranteed to be unchanging in this file.\n# At a later date when this is more set in stone, this warning will be removed.\n# At said later date, there should also be things added to support extra formatter\n# registra... | diff --git a/redbot/core/commands/help.py b/redbot/core/commands/help.py
index f1f590e219d..3f671df7e08 100644
--- a/redbot/core/commands/help.py
+++ b/redbot/core/commands/help.py
@@ -249,6 +249,12 @@ async def make_and_send_embeds(self, ctx, embed_dict: dict):
author_info = {"name": f"{ctx.me.display_name}... |
google__jax-11544 | `lax.conv_general_dilated` will crash when `padding` has some negative
`lax.conv_general_dilated` will crash when `padding` has some negative
```py
import jax
def fn(lhs, rhs):
window_strides = [1, 1]
padding = [[5, 16], [-5, 1]]
return jax.lax.conv_general_dilated(lhs, rhs, window_strides, padding)... | [
{
"content": "# Copyright 2018 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicab... | [
{
"content": "# Copyright 2018 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicab... | diff --git a/jax/_src/lax/convolution.py b/jax/_src/lax/convolution.py
index e73d147ad499..1c31fe6a361f 100644
--- a/jax/_src/lax/convolution.py
+++ b/jax/_src/lax/convolution.py
@@ -812,6 +812,9 @@ def conv_shape_tuple(lhs_shape, rhs_shape, strides, pads, batch_group_count=1):
lhs_padded = np.add(lhs_shape[2:], n... |
python__mypy-4522 | Allow 'x_' flags in the [mypy-foo] section of mypy.ini
This is a feature request.
I'd like to use the `[mypy-foo]` sections in `mypy.ini` to control behavior of some other tool (so I can have a centralized config file -- the tool is a linter that requires annotations if a certain flag is set). I can't reuse an exist... | [
{
"content": "\"\"\"Mypy type checker command line tool.\"\"\"\n\nimport argparse\nimport configparser\nimport fnmatch\nimport os\nimport re\nimport sys\nimport time\n\nfrom typing import Any, Dict, List, Mapping, Optional, Sequence, Set, Tuple, Callable\n\nfrom mypy import build\nfrom mypy import defaults\nfro... | [
{
"content": "\"\"\"Mypy type checker command line tool.\"\"\"\n\nimport argparse\nimport configparser\nimport fnmatch\nimport os\nimport re\nimport sys\nimport time\n\nfrom typing import Any, Dict, List, Mapping, Optional, Sequence, Set, Tuple, Callable\n\nfrom mypy import build\nfrom mypy import defaults\nfro... | diff --git a/mypy/main.py b/mypy/main.py
index a418dfa6cf8d..90b1285abc51 100644
--- a/mypy/main.py
+++ b/mypy/main.py
@@ -755,6 +755,8 @@ def parse_section(prefix: str, template: Options,
print("%s: Unrecognized report type: %s" % (prefix, orig_key),
file=sys.std... |
ESMCI__cime-2444 | CESM provenance fails if you don't have write permission in SRCROOT
If you don't have write permission in SRCROOT, the CESM provenance function fails, and thus the build ends in an error. This is because manage_externals tries to write to the log file in SRCROOT and fails to do so.
I have a fix incoming.
| [
{
"content": "#!/usr/bin/env python\n\n\"\"\"\nLibrary for saving build/run provenance.\n\"\"\"\n\nfrom CIME.XML.standard_module_setup import *\nfrom CIME.utils import touch, gzip_existing_file, SharedArea, copy_umask, convert_to_babylonian_time, get_current_commit\n\nimport tarfile, getpass, signal, glob, shut... | [
{
"content": "#!/usr/bin/env python\n\n\"\"\"\nLibrary for saving build/run provenance.\n\"\"\"\n\nfrom CIME.XML.standard_module_setup import *\nfrom CIME.utils import touch, gzip_existing_file, SharedArea, copy_umask, convert_to_babylonian_time, get_current_commit\n\nimport tarfile, getpass, signal, glob, shut... | diff --git a/scripts/lib/CIME/provenance.py b/scripts/lib/CIME/provenance.py
index 14ee01e0f26..39bcef865bb 100644
--- a/scripts/lib/CIME/provenance.py
+++ b/scripts/lib/CIME/provenance.py
@@ -83,7 +83,7 @@ def _save_build_provenance_cesm(case, lid): # pylint: disable=unused-argument
manic = os.path.join(srcroot, ... |
nvaccess__nvda-12182 | When testing with NVDA version alpha-21882, the input gesture interface could not be opened
### Steps to reproduce:
First open the NVDA Menu/Preferences submenu, then find the input gesture, and then enter to open the menu.
### Actual behavior:
This is when the NVDA error tone appears
### Expected behavior:
Ca... | [
{
"content": "# -*- coding: UTF-8 -*-\r\n# A part of NonVisual Desktop Access (NVDA)\r\n# Copyright (C) 2020 NV Access Limited\r\n# This file is covered by the GNU General Public License.\r\n# See the file COPYING for more details.\r\n\r\nimport itertools\r\nimport re\r\nfrom typing import Tuple, Union, Dict\r\... | [
{
"content": "# -*- coding: UTF-8 -*-\r\n# A part of NonVisual Desktop Access (NVDA)\r\n# Copyright (C) 2020 NV Access Limited\r\n# This file is covered by the GNU General Public License.\r\n# See the file COPYING for more details.\r\n\r\nimport itertools\r\nimport re\r\nfrom typing import Tuple, Union, Dict\r\... | diff --git a/source/gui/inputGestures.py b/source/gui/inputGestures.py
index 8920275ed82..82dbb9808a9 100644
--- a/source/gui/inputGestures.py
+++ b/source/gui/inputGestures.py
@@ -608,7 +608,7 @@ def makeSettings(self, settingsSizer):
bHelper.sizer.AddStretchSpacer()
# Translators: The label of a button to rese... |
iterative__dvc-8738 | DVC Repro incorrectly saving directory to cache
## Bug Report
UPDATE: Skip to https://github.com/iterative/dvc/issues/4428#issuecomment-778259232
As the title states. I run `dvc repro extract` for my `extract` pipeline stage. This takes two pretty large zip files and extracts them into specified folders. These fo... | [
{
"content": "import logging\nimport os\nimport string\nfrom collections import defaultdict\nfrom contextlib import suppress\nfrom dataclasses import dataclass\nfrom typing import TYPE_CHECKING, Any, Dict, Optional, Set\n\nfrom funcy import cached_property, project\n\nfrom dvc import prompt\nfrom dvc.exceptions... | [
{
"content": "import logging\nimport os\nimport string\nfrom collections import defaultdict\nfrom contextlib import suppress\nfrom dataclasses import dataclass\nfrom typing import TYPE_CHECKING, Any, Dict, Optional, Set\n\nfrom funcy import cached_property, project\n\nfrom dvc import prompt\nfrom dvc.exceptions... | diff --git a/dvc/stage/__init__.py b/dvc/stage/__init__.py
index 64070ddd5b..7a9ed63dbf 100644
--- a/dvc/stage/__init__.py
+++ b/dvc/stage/__init__.py
@@ -591,7 +591,15 @@ def run(
if kwargs.get("checkpoint_func", None) or no_download:
allow_missing = True
- self.save(allow_mi... |
keras-team__keras-13477 | ValueError: Unable to create group (Name already exists) with model.save_weights()
This is a similar issue to https://github.com/keras-team/keras/issues/6005 but I believe it is caused by the way `h5py` defines groups. In particular, if a layer named `foo` is in a network after a layer named `foo/bar`, `h5py` throws an... | [
{
"content": "\"\"\"Model saving utilities.\n\"\"\"\nfrom __future__ import print_function\nfrom __future__ import absolute_import\nfrom __future__ import division\n\nimport os\nimport json\nimport yaml\nimport inspect\nimport warnings\nimport tempfile\nfrom six.moves import zip\nfrom six import string_types\nf... | [
{
"content": "\"\"\"Model saving utilities.\n\"\"\"\nfrom __future__ import print_function\nfrom __future__ import absolute_import\nfrom __future__ import division\n\nimport os\nimport json\nimport yaml\nimport inspect\nimport warnings\nimport tempfile\nfrom six.moves import zip\nfrom six import string_types\nf... | diff --git a/keras/engine/saving.py b/keras/engine/saving.py
index 95af7dd14c8c..ba3beb657849 100644
--- a/keras/engine/saving.py
+++ b/keras/engine/saving.py
@@ -744,7 +744,9 @@ def save_weights_to_hdf5_group(group, layers):
group.attrs['backend'] = K.backend().encode('utf8')
group.attrs['keras_version'] = s... |
qtile__qtile-1659 | qtile-top crash 'Client' object has no attribute 'tracemalloc_dump'
<!--
Please do not ask general questions here! There are [community
contact](https://github.com/qtile/qtile#community) options for that.
-->
# Issue description
<!--
A brief discussion of what failed and how it failed. A description of
what ... | [
{
"content": "# Copyright (c) 2015, Roger Duran\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, m... | [
{
"content": "# Copyright (c) 2015, Roger Duran\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, m... | diff --git a/libqtile/scripts/qtile_top.py b/libqtile/scripts/qtile_top.py
index f6df9908a1..c6df228aaa 100644
--- a/libqtile/scripts/qtile_top.py
+++ b/libqtile/scripts/qtile_top.py
@@ -30,7 +30,7 @@
import tracemalloc
from tracemalloc import Snapshot
-from libqtile import ipc
+from libqtile import command_client,... |
zulip__zulip-5459 | "Link to this conversation" with group pm conversations isn't shareable
If I have a conversation with alice@example.com and bob@example.com, and want to email them a permanent link to the conversation using the "Link to this conversation" feature, it doesn't work, because we generate a link of the form:
`/#narrow/ne... | [
{
"content": "from __future__ import absolute_import\n\nfrom django.utils.translation import ugettext as _\nfrom django.utils.timezone import now as timezone_now\nfrom django.conf import settings\nfrom django.core import validators\nfrom django.core.exceptions import ValidationError\nfrom django.db import conne... | [
{
"content": "from __future__ import absolute_import\n\nfrom django.utils.translation import ugettext as _\nfrom django.utils.timezone import now as timezone_now\nfrom django.conf import settings\nfrom django.core import validators\nfrom django.core.exceptions import ValidationError\nfrom django.db import conne... | diff --git a/frontend_tests/node_tests/filter.js b/frontend_tests/node_tests/filter.js
index eb045fdaf0205..e7109340d5492 100644
--- a/frontend_tests/node_tests/filter.js
+++ b/frontend_tests/node_tests/filter.js
@@ -310,6 +310,19 @@ function make_sub(name, stream_id) {
}));
assert(!predicate({type: 'stream'}... |
Pyomo__pyomo-1806 | pyomo.common.tempfiles.TempfileManager raises FileNotFoundError
I've recently started using pyomo.common.tempfiles.TempfileManager to set the temporary directory in a package, as in https://pyomo.readthedocs.io/en/stable/working_models.html#changing-the-temporary-directory. I was previously using TempfileManager from P... | [
{
"content": "# ___________________________________________________________________________\n#\n# Pyomo: Python Optimization Modeling Objects\n# Copyright 2017 National Technology and Engineering Solutions of Sandia, LLC\n# Under the terms of Contract DE-NA0003525 with National Technology and\n# Engineerin... | [
{
"content": "# ___________________________________________________________________________\n#\n# Pyomo: Python Optimization Modeling Objects\n# Copyright 2017 National Technology and Engineering Solutions of Sandia, LLC\n# Under the terms of Contract DE-NA0003525 with National Technology and\n# Engineerin... | diff --git a/pyomo/common/tempfiles.py b/pyomo/common/tempfiles.py
index 7f5c07cd122..d9a28f664fd 100644
--- a/pyomo/common/tempfiles.py
+++ b/pyomo/common/tempfiles.py
@@ -34,9 +34,8 @@
class TempfileManagerClass:
"""A class that manages temporary files."""
- tempdir = None
-
def __init__(self, **kwds)... |
unionai-oss__pandera-500 | Better error message when validating pandas.MultiIndex with a pandera.Index schema
**Describe the bug**
Trying to validate a MultiIndex data frame with a schema that Index has been set to single Index.
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the... | [
{
"content": "\"\"\"Components used in pandera schemas.\"\"\"\n\nimport warnings\nfrom copy import copy, deepcopy\nfrom typing import Any, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport pandas as pd\n\nfrom . import errors\nfrom . import strategies as st\nfrom .error_handlers import SchemaErro... | [
{
"content": "\"\"\"Components used in pandera schemas.\"\"\"\n\nimport warnings\nfrom copy import copy, deepcopy\nfrom typing import Any, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport pandas as pd\n\nfrom . import errors\nfrom . import strategies as st\nfrom .error_handlers import SchemaErro... | diff --git a/pandera/schema_components.py b/pandera/schema_components.py
index 5b3b3d912..14b457d1e 100644
--- a/pandera/schema_components.py
+++ b/pandera/schema_components.py
@@ -354,6 +354,11 @@ def validate(
otherwise creates a copy of the data.
:returns: validated DataFrame or Series.
... |
MongoEngine__mongoengine-1860 | Querying EmbeddedDocumentListField with operators 'Client' object has no attribute 'name__icontains'
I have an EmbeddedDocumentListField with Following Fields
Clients [{"name","email","mobile",..},.....]
now i am able to filter pure on pure fld=value
but unable to use any of the operators along with the fields... | [
{
"content": "import itertools\nimport weakref\n\nfrom bson import DBRef\nimport six\n\nfrom mongoengine.common import _import_class\nfrom mongoengine.errors import DoesNotExist, MultipleObjectsReturned\n\n__all__ = ('BaseDict', 'BaseList', 'EmbeddedDocumentList', 'LazyReference')\n\n\nclass BaseDict(dict):\n ... | [
{
"content": "import itertools\nimport weakref\n\nfrom bson import DBRef\nimport six\n\nfrom mongoengine.common import _import_class\nfrom mongoengine.errors import DoesNotExist, MultipleObjectsReturned\n\n__all__ = ('BaseDict', 'BaseList', 'EmbeddedDocumentList', 'LazyReference')\n\n\nclass BaseDict(dict):\n ... | diff --git a/mongoengine/base/datastructures.py b/mongoengine/base/datastructures.py
index 8948243e4..db292f144 100644
--- a/mongoengine/base/datastructures.py
+++ b/mongoengine/base/datastructures.py
@@ -234,6 +234,9 @@ def filter(self, **kwargs):
Filters the list by only including embedded documents with the... |
pretix__pretix-241 | Substring search when matching voucher tags
I might use a tagging structure such as "DE-Berlin", "DE-Stuttgart" for vouchers, and it'd be neat to match all "DE-" tagged vouchers at once.
| [
{
"content": "import csv\nimport io\n\nfrom django.conf import settings\nfrom django.contrib import messages\nfrom django.core.urlresolvers import resolve, reverse\nfrom django.db import transaction\nfrom django.db.models import Count, Q, Sum\nfrom django.http import (\n Http404, HttpResponse, HttpResponseBa... | [
{
"content": "import csv\nimport io\n\nfrom django.conf import settings\nfrom django.contrib import messages\nfrom django.core.urlresolvers import resolve, reverse\nfrom django.db import transaction\nfrom django.db.models import Count, Q, Sum\nfrom django.http import (\n Http404, HttpResponse, HttpResponseBa... | diff --git a/src/pretix/control/views/vouchers.py b/src/pretix/control/views/vouchers.py
index 5ea01d68899..c045202bdfc 100644
--- a/src/pretix/control/views/vouchers.py
+++ b/src/pretix/control/views/vouchers.py
@@ -37,7 +37,7 @@ def get_queryset(self):
qs = qs.filter(Q(code__icontains=s) | Q(tag__icontai... |
conda__conda-6656 | conda update --name AssertionError
```
error: AssertionError()
command: /Users/cpthgli/.anyenv/envs/pyenv/versions/default/bin/conda update --name --all --yes
user_agent: conda/4.4.4 requests/2.18.4 CPython/3.6.4 Darwin/17.3.0 OSX/10.13.2
_messageid: -9223372036843525540
_messagetime: 1514462637935 / 2017-12-28 0... | [
{
"content": "# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import, division, print_function, unicode_literals\n\nfrom errno import ENOENT\nfrom logging import getLogger\nimport os\nfrom os.path import (abspath, basename, dirname, expanduser, isdir, isfile, join, normpath,\n split... | [
{
"content": "# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import, division, print_function, unicode_literals\n\nfrom errno import ENOENT\nfrom logging import getLogger\nimport os\nfrom os.path import (abspath, basename, dirname, expanduser, isdir, isfile, join, normpath,\n split... | diff --git a/conda/base/context.py b/conda/base/context.py
index 16bea2ed73a..60b8c4a931d 100644
--- a/conda/base/context.py
+++ b/conda/base/context.py
@@ -968,6 +968,14 @@ def determine_target_prefix(ctx, args=None):
except AttributeError:
prefix_path = None
+ if prefix_name is not None and not pre... |
privacyidea__privacyidea-1597 | Unable to Enroll Push Token if tokenlabel is configured in policy
# What did you try to do?
Enroll push token for user with **tokenlabel** set to \<u>_\<s> within an enrollment policy. Username is testuser and token serial is PIPU00001234
# What outcome did you expect?
Token enrolled successfully in app.
... | [
{
"content": "# -*- coding: utf-8 -*-\n#\n# License: AGPLv3\n# contact: http://www.privacyidea.org\n#\n# 2019-02-08 Cornelius Kölbel <cornelius.koelbel@netknights.it>\n# Start the pushtoken class\n#\n# This code is free software; you can redistribute it and/or\n# modify it under the terms o... | [
{
"content": "# -*- coding: utf-8 -*-\n#\n# License: AGPLv3\n# contact: http://www.privacyidea.org\n#\n# 2019-02-08 Cornelius Kölbel <cornelius.koelbel@netknights.it>\n# Start the pushtoken class\n#\n# This code is free software; you can redistribute it and/or\n# modify it under the terms o... | diff --git a/privacyidea/lib/tokens/pushtoken.py b/privacyidea/lib/tokens/pushtoken.py
index e6f5da34d1..bff080d77c 100644
--- a/privacyidea/lib/tokens/pushtoken.py
+++ b/privacyidea/lib/tokens/pushtoken.py
@@ -343,6 +343,7 @@ def get_init_detail(self, params=None, user=None):
extra_data[k] = fb_option... |
sopel-irc__sopel-1587 | admin: admins can usurp owners
I believe `admin` and `owner` are meant to be two separate permission levels for the bot. There are `@require_admin` and `@require_owner` for commands, implying that there are certain functions for an `owner` that should not be available to `admin`s.
That being said, if the `admin` p... | [
{
"content": "# coding=utf-8\n\"\"\"\nadmin.py - Sopel Admin Module\nCopyright 2010-2011, Sean B. Palmer (inamidst.com) and Michael Yanovich\n(yanovich.net)\nCopyright © 2012, Elad Alfassa, <elad@fedoraproject.org>\nCopyright 2013, Ari Koivula <ari@koivu.la>\n\nLicensed under the Eiffel Forum License 2.\n\nhttp... | [
{
"content": "# coding=utf-8\n\"\"\"\nadmin.py - Sopel Admin Module\nCopyright 2010-2011, Sean B. Palmer (inamidst.com) and Michael Yanovich\n(yanovich.net)\nCopyright © 2012, Elad Alfassa, <elad@fedoraproject.org>\nCopyright 2013, Ari Koivula <ari@koivu.la>\n\nLicensed under the Eiffel Forum License 2.\n\nhttp... | diff --git a/sopel/modules/admin.py b/sopel/modules/admin.py
index 22fc0f93e6..3191f8916f 100644
--- a/sopel/modules/admin.py
+++ b/sopel/modules/admin.py
@@ -220,6 +220,13 @@ def set_config(bot, trigger):
bot.reply("%s.%s = %s" % (section_name, option, value))
return
+ # 'core.owner' cannot be s... |
lutris__lutris-3944 | Won't open configure panel with some icons
```
2021-11-03 02:59:23,691: Unable to load icon from image fl-studio
Traceback (most recent call last):
File "/usr/lib/python3/dist-packages/lutris/game_actions.py", line 178, in on_edit_game_configuration
EditGameConfigDialog(self.window, self.game)
File "/usr/l... | [
{
"content": "\"\"\"Various utilities using the GObject framework\"\"\"\nimport array\nimport os\n\nfrom gi.repository import Gdk, GdkPixbuf, Gio, GLib, Gtk\n\nfrom lutris import settings\nfrom lutris.util import datapath, system\nfrom lutris.util.log import logger\n\ntry:\n from PIL import Image\nexcept Imp... | [
{
"content": "\"\"\"Various utilities using the GObject framework\"\"\"\nimport array\nimport os\n\nfrom gi.repository import Gdk, GdkPixbuf, Gio, GLib, Gtk\n\nfrom lutris import settings\nfrom lutris.util import datapath, system\nfrom lutris.util.log import logger\n\ntry:\n from PIL import Image\nexcept Imp... | diff --git a/lutris/gui/widgets/utils.py b/lutris/gui/widgets/utils.py
index 52de2a51db..5f9ea11990 100644
--- a/lutris/gui/widgets/utils.py
+++ b/lutris/gui/widgets/utils.py
@@ -54,7 +54,7 @@ def get_pixbuf(image, size, fallback=None, is_installed=True):
fallback = get_default_icon(size)
if syste... |
pulp__pulpcore-2558 | Implementation of does_batch in DeclarativeContent is wrong
**Describe the bug**
`does_batch` is supposed to decide, whether content can be waited on, or must be evaluated asap. It produces `False` unnecessarily often, leading to performance issues.
**Additional context**
This is suspected to at least one of the r... | [
{
"content": "from gettext import gettext as _\n\nimport asyncio\n\nfrom pulpcore.constants import ALL_KNOWN_CONTENT_CHECKSUMS\nfrom pulpcore.plugin.models import Artifact\n\n\nclass DeclarativeArtifact:\n \"\"\"\n Relates an :class:`~pulpcore.plugin.models.Artifact`, how to download it, and its\n `rel... | [
{
"content": "from gettext import gettext as _\n\nimport asyncio\n\nfrom pulpcore.constants import ALL_KNOWN_CONTENT_CHECKSUMS\nfrom pulpcore.plugin.models import Artifact\n\n\nclass DeclarativeArtifact:\n \"\"\"\n Relates an :class:`~pulpcore.plugin.models.Artifact`, how to download it, and its\n `rel... | diff --git a/CHANGES/2557.bugfix b/CHANGES/2557.bugfix
new file mode 100644
index 0000000000..097b40ae8a
--- /dev/null
+++ b/CHANGES/2557.bugfix
@@ -0,0 +1 @@
+Fixed `does_batch` method in sync pipeline to allow waiting on content that is already resolved.
diff --git a/pulpcore/plugin/stages/models.py b/pulpcore/plugin... |
beeware__toga-645 | ImageView example not working on Linux
## Expected Behavior
<!--- If you're describing a bug, tell us what you expect to happen. -->
ImageView demos display both a local image file and one from a web url
<!--- If you're requesting a new feature, tell us why you'd like this feature. -->
## Current Behavior
<!--... | [
{
"content": "import os\nimport toga\nfrom toga.style.pack import *\n\nclass ImageViewApp(toga.App):\n def startup(self):\n self.main_window = toga.MainWindow(title=self.name)\n \n box = toga.Box()\n box.style.padding = 40\n box.style.update(alignment=CENTER)\n box.s... | [
{
"content": "import os\nimport toga\nfrom toga.style.pack import *\n\nclass ImageViewApp(toga.App):\n def startup(self):\n self.main_window = toga.MainWindow(title=self.name)\n \n box = toga.Box()\n box.style.padding = 40\n box.style.update(alignment=CENTER)\n box.s... | diff --git a/examples/imageview/imageview/app.py b/examples/imageview/imageview/app.py
index 77a4d5e3c7..62b532e0e1 100644
--- a/examples/imageview/imageview/app.py
+++ b/examples/imageview/imageview/app.py
@@ -14,7 +14,7 @@ def startup(self):
# image from local path
# load brutus.png from the package... |
vacanza__python-holidays-474 | Add Juneteenth
https://www.nytimes.com/2021/06/17/us/politics/juneteenth-holiday-biden.html
| [
{
"content": "# -*- coding: utf-8 -*-\n\n# python-holidays\n# ---------------\n# A fast, efficient Python library for generating country, province and state\n# specific sets of holidays on the fly. It aims to make determining whether a\n# specific date is a holiday as fast and flexible as possible.\n#\n# ... | [
{
"content": "# -*- coding: utf-8 -*-\n\n# python-holidays\n# ---------------\n# A fast, efficient Python library for generating country, province and state\n# specific sets of holidays on the fly. It aims to make determining whether a\n# specific date is a holiday as fast and flexible as possible.\n#\n# ... | diff --git a/holidays/countries/united_states.py b/holidays/countries/united_states.py
index fa3a23da2..4be7f73c0 100644
--- a/holidays/countries/united_states.py
+++ b/holidays/countries/united_states.py
@@ -375,6 +375,10 @@ def _populate(self, year):
elif date(year, JUN, 11).weekday() == SUN:
... |
linz__geostore-790 | Improve metadata description for catalog metadata
### User Story
<!-- A user story to describe why a user wants to do something, who the user is and what they want to do -->
So that I can understand more about the catalog metadata for the Geostore, as an external data analyst or developer, I want a good description a... | [
{
"content": "from json import dumps\nfrom typing import TYPE_CHECKING\n\nimport boto3\nfrom pystac import STAC_IO, Catalog, CatalogType, Collection, Item # type: ignore[import]\nfrom pystac.layout import HrefLayoutStrategy # type: ignore[import]\n\nfrom ..api_keys import EVENT_KEY\nfrom ..api_responses impor... | [
{
"content": "from json import dumps\nfrom typing import TYPE_CHECKING\n\nimport boto3\nfrom pystac import STAC_IO, Catalog, CatalogType, Collection, Item # type: ignore[import]\nfrom pystac.layout import HrefLayoutStrategy # type: ignore[import]\n\nfrom ..api_keys import EVENT_KEY\nfrom ..api_responses impor... | diff --git a/backend/populate_catalog/task.py b/backend/populate_catalog/task.py
index af9c519b7..f7f2c9bde 100644
--- a/backend/populate_catalog/task.py
+++ b/backend/populate_catalog/task.py
@@ -32,8 +32,12 @@
S3_CLIENT: S3Client = boto3.client("s3")
ROOT_CATALOG_ID = "root_catalog"
-ROOT_CATALOG_TITLE = "Geostor... |
nltk__nltk-2810 | usage function: repair or drop?
Do we need to support this usage method?
https://github.com/nltk/nltk/blob/develop/nltk/util.py#L40
Here's a discussion of it's shortcomings:
https://github.com/nltk/nltk/pull/2801#discussion_r706567025
| [
{
"content": "# Natural Language Toolkit: Utility functions\n#\n# Copyright (C) 2001-2021 NLTK Project\n# Author: Steven Bird <stevenbird1@gmail.com>\n# Eric Kafe <kafe.eric@gmail.com> (acyclic closures)\n# URL: <http://nltk.org/>\n# For license information, see LICENSE.TXT\n\nimport bisect\nimport insp... | [
{
"content": "# Natural Language Toolkit: Utility functions\n#\n# Copyright (C) 2001-2021 NLTK Project\n# Author: Steven Bird <stevenbird1@gmail.com>\n# Eric Kafe <kafe.eric@gmail.com> (acyclic closures)\n# URL: <http://nltk.org/>\n# For license information, see LICENSE.TXT\n\nimport bisect\nimport insp... | diff --git a/nltk/test/classify.doctest b/nltk/test/classify.doctest
index bfdf17b1ef..3e1319d312 100644
--- a/nltk/test/classify.doctest
+++ b/nltk/test/classify.doctest
@@ -11,16 +11,13 @@
Classifiers label tokens with category labels (or *class labels*).
Typically, labels are represented with strings (such as ``"h... |
streamlit__streamlit-265 | On rerun, faded elements are becoming opaque one by one. Instead they become opaque all at once.
Steps to repro:
1. Run `examples/reference.py`
2. When done, rerun it.
**Expected:** on rerun, all elements fade out and then become opaque one by one even before the run is done.
**Actual:** on rerun, all elements fa... | [
{
"content": "# -*- coding: utf-8 -*-\n# Copyright 2018-2019 Streamlit Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n... | [
{
"content": "# -*- coding: utf-8 -*-\n# Copyright 2018-2019 Streamlit Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n... | diff --git a/lib/streamlit/watcher/LocalSourcesWatcher.py b/lib/streamlit/watcher/LocalSourcesWatcher.py
index 49f837525911..edfec054b341 100644
--- a/lib/streamlit/watcher/LocalSourcesWatcher.py
+++ b/lib/streamlit/watcher/LocalSourcesWatcher.py
@@ -194,9 +194,13 @@ def update_watched_modules(self):
def _file_is_... |
yt-dlp__yt-dlp-8399 | "ERROR: Unable to download video subtitles for 'en': [Errno 2] No such file or directory" with m3u8 webvtt subs
### Checklist
- [X] I'm reporting a bug unrelated to a specific site
- [X] I've verified that I'm running yt-dlp version **2022.07.18** ([update instructions](https://github.com/yt-dlp/yt-dlp#update)) or ... | [
{
"content": "import os\nimport random\nimport time\n\nfrom .common import FileDownloader\nfrom ..networking import Request\nfrom ..networking.exceptions import (\n CertificateVerifyError,\n HTTPError,\n TransportError,\n)\nfrom ..utils import (\n ContentTooShortError,\n RetryManager,\n Thrott... | [
{
"content": "import os\nimport random\nimport time\n\nfrom .common import FileDownloader\nfrom ..networking import Request\nfrom ..networking.exceptions import (\n CertificateVerifyError,\n HTTPError,\n TransportError,\n)\nfrom ..utils import (\n ContentTooShortError,\n RetryManager,\n Thrott... | diff --git a/yt_dlp/downloader/http.py b/yt_dlp/downloader/http.py
index f5237443e212..693828b6e5e7 100644
--- a/yt_dlp/downloader/http.py
+++ b/yt_dlp/downloader/http.py
@@ -237,8 +237,13 @@ def download():
def retry(e):
close_stream()
- ctx.resume_len = (byte_counter if ... |
liqd__a4-opin-2485 | Community debate module error messages
**expected behaviour:** Error message only shown for document upload if file is incorrect
**behaviour:** Error message is also shown when title is not added
| [
{
"content": "from django.contrib import messages\nfrom django.shortcuts import render\nfrom django.urls import reverse\nfrom django.utils.translation import gettext as _\nfrom django.views import generic\nfrom rules.contrib.views import PermissionRequiredMixin\n\nfrom adhocracy4.filters import views as filter_... | [
{
"content": "from django.contrib import messages\nfrom django.shortcuts import render\nfrom django.urls import reverse\nfrom django.utils.translation import gettext as _\nfrom django.views import generic\nfrom rules.contrib.views import PermissionRequiredMixin\n\nfrom adhocracy4.filters import views as filter_... | diff --git a/euth/communitydebate/views.py b/euth/communitydebate/views.py
index cb16d590d..e6b4b3f58 100644
--- a/euth/communitydebate/views.py
+++ b/euth/communitydebate/views.py
@@ -95,8 +95,9 @@ def post(self, request, *args, **kwargs):
'successfully created'))
... |
pyqtgraph__pyqtgraph-905 | [CI-fail] HDF5 big data
This test fails due to h5py missing. Probably we can change requisites to include h5py or skip this test if h5py is not found.
```
examples/test_examples.py::test_examples[PyQt4-f17] frontend = PyQt4. f = ('HDF5 big data', 'hdf5.py')
HDF5 big data.....................................FAILED
P... | [
{
"content": "from __future__ import division, print_function, absolute_import\nimport subprocess\nimport time\nimport os\nimport sys\nimport errno\nfrom pyqtgraph.pgcollections import OrderedDict\nfrom pyqtgraph.python2_3 import basestring\n\npath = os.path.abspath(os.path.dirname(__file__))\n\n\nexamples = Or... | [
{
"content": "from __future__ import division, print_function, absolute_import\nimport subprocess\nimport time\nimport os\nimport sys\nimport errno\nimport copy\nfrom pyqtgraph.pgcollections import OrderedDict\nfrom pyqtgraph.python2_3 import basestring\n\npath = os.path.abspath(os.path.dirname(__file__))\n\n\n... | diff --git a/examples/test_examples.py b/examples/test_examples.py
index d8de370fa0..c5997348d4 100644
--- a/examples/test_examples.py
+++ b/examples/test_examples.py
@@ -41,7 +41,7 @@ def flaky_print(*args):
"pypi\n\npip install importlib\n\n")
pass
-files = utils.buildFileList(utils.examples)
+files... |
pyinstaller__pyinstaller-8378 | Mac OS Catalina App crash on app start
1. Create mac os app using PyInstaller build.spec
2. Set "LSBackgroundOnly" to "false" in "Contents/Info.plist"
3. Open App... loading.. loading.. crash
build app on mac os Catalina, it crashes when i launch app.
on Mojave everything worked perfectly
Versions:
PyInstal... | [
{
"content": "#-----------------------------------------------------------------------------\n# Copyright (c) 2005-2023, PyInstaller Development Team.\n#\n# Distributed under the terms of the GNU General Public License (version 2\n# or later) with exception for distributing the bootloader.\n#\n# The full licens... | [
{
"content": "#-----------------------------------------------------------------------------\n# Copyright (c) 2005-2023, PyInstaller Development Team.\n#\n# Distributed under the terms of the GNU General Public License (version 2\n# or later) with exception for distributing the bootloader.\n#\n# The full licens... | diff --git a/PyInstaller/building/osx.py b/PyInstaller/building/osx.py
index c56f40d3d4..a0cf924db8 100644
--- a/PyInstaller/building/osx.py
+++ b/PyInstaller/building/osx.py
@@ -58,7 +58,10 @@ def __init__(self, *args, **kwargs):
self.name = os.path.join(CONF['distpath'], base_name)
self.appname = ... |
Mailu__Mailu-2651 | plus mail in auto-forward destination
The email address in `admin -> User settings -> Auto-forward -> Destination` cannot contains a '+' character (error: Invalid email address). But the '+' in email address is valid and admin should accept it.
| [
{
"content": "from wtforms import validators, fields, widgets\nfrom wtforms_components import fields as fields_\nfrom flask_babel import lazy_gettext as _\n\nimport flask_login\nimport flask_wtf\nimport re\n\nLOCALPART_REGEX = \"^[a-zA-Z0-9!#$%&'*+/=?^_`{|}~-]+(?:\\.[a-zA-Z0-9!#$%&'*+/=?^_`{|}~-]+)*$\"\n\nclass... | [
{
"content": "from wtforms import validators, fields, widgets\nfrom wtforms_components import fields as fields_\nfrom flask_babel import lazy_gettext as _\n\nimport flask_login\nimport flask_wtf\nimport re\n\nLOCALPART_REGEX = \"^[a-zA-Z0-9!#$%&'*+/=?^_`{|}~-]+(?:\\.[a-zA-Z0-9!#$%&'*+/=?^_`{|}~-]+)*$\"\n\nclass... | diff --git a/core/admin/mailu/ui/forms.py b/core/admin/mailu/ui/forms.py
index c6335559a..79c764504 100644
--- a/core/admin/mailu/ui/forms.py
+++ b/core/admin/mailu/ui/forms.py
@@ -37,7 +37,7 @@ def __init__(self,message=_('Invalid email address.')):
self.message = message
def __call__(self, form, field... |
fossasia__open-event-server-4882 | Session allowed to be created without an associated Track
**Describe the bug**
<!-- A clear and concise description of what the bug is. -->
Sessions can be created without being associated with a Track
**Expected behavior**
<!-- A clear and concise description of what you expected to happen. -->
It shouldn't be... | [
{
"content": "from flask_rest_jsonapi import ResourceDetail, ResourceList, ResourceRelationship\n\nfrom app.api.bootstrap import api\nfrom app.api.events import Event\nfrom app.api.helpers.db import safe_query, get_count\nfrom app.api.helpers.exceptions import ForbiddenException\nfrom app.api.helpers.mail impor... | [
{
"content": "from flask_rest_jsonapi import ResourceDetail, ResourceList, ResourceRelationship\n\nfrom app.api.bootstrap import api\nfrom app.api.events import Event\nfrom app.api.helpers.db import safe_query, get_count\nfrom app.api.helpers.exceptions import ForbiddenException\nfrom app.api.helpers.mail impor... | diff --git a/app/api/sessions.py b/app/api/sessions.py
index 1a04ce1071..6c86f9b056 100644
--- a/app/api/sessions.py
+++ b/app/api/sessions.py
@@ -32,7 +32,7 @@ def before_post(self, args, kwargs, data):
:param data:
:return:
"""
- require_relationship(['event'], data)
+ require... |
django-cms__django-cms-2655 | unfolding subitem in page tree leaves them without title
See attached file. When I am unfolding a branch of the tree, the page names are not there. Upon reloading the page, all the unfolded branches contain their page names.
When inspecting the DOM, the a tag is there, but no content.
![branches-without-title](https:... | [
{
"content": "# -*- coding: utf-8 -*-\n\n\nfrom django.utils.timezone import now\n\nfrom os.path import join\nfrom cms import constants\nfrom cms.constants import PUBLISHER_STATE_DEFAULT, PUBLISHER_STATE_PENDING, PUBLISHER_STATE_DIRTY, TEMPLATE_INHERITANCE_MAGIC\nfrom cms.exceptions import PublicIsUnmodifiable,... | [
{
"content": "# -*- coding: utf-8 -*-\n\n\nfrom django.utils.timezone import now\n\nfrom os.path import join\nfrom cms import constants\nfrom cms.constants import PUBLISHER_STATE_DEFAULT, PUBLISHER_STATE_PENDING, PUBLISHER_STATE_DIRTY, TEMPLATE_INHERITANCE_MAGIC\nfrom cms.exceptions import PublicIsUnmodifiable,... | diff --git a/cms/models/pagemodel.py b/cms/models/pagemodel.py
index defce42274a..4f69a929319 100644
--- a/cms/models/pagemodel.py
+++ b/cms/models/pagemodel.py
@@ -729,7 +729,7 @@ def validate_title(title):
if not hasattr(self, 'title_cache'):
self.title_cache = {}
- for title in sel... |
django-cms__django-cms-3856 | Frontend glitch for get_extra_local_plugin_menu_items()
I'm trying to add an additional item to content menu for a custom plugin. So I added this to my plugin.
```
def get_extra_local_plugin_menu_items(self, request, plugin):
return [
PluginMenuItem(
_('Preview Wrapper'),
... | [
{
"content": "# -*- coding: utf-8 -*-\nfrom copy import copy\nfrom datetime import datetime\nfrom itertools import chain\nimport re\nfrom classytags.values import StringValue\nfrom cms.utils.urlutils import admin_reverse\n\nfrom django import template\nfrom django.conf import settings\nfrom django.contrib.sites... | [
{
"content": "# -*- coding: utf-8 -*-\nfrom copy import copy\nfrom datetime import datetime\nfrom itertools import chain\nimport re\nfrom classytags.values import StringValue\nfrom cms.utils.urlutils import admin_reverse\n\nfrom django import template\nfrom django.conf import settings\nfrom django.contrib.sites... | diff --git a/cms/templatetags/cms_tags.py b/cms/templatetags/cms_tags.py
index 8a582b3cfc7..50a3207f4a4 100644
--- a/cms/templatetags/cms_tags.py
+++ b/cms/templatetags/cms_tags.py
@@ -439,7 +439,7 @@ def get_context(self, context, obj):
plugin_class_inst = plugin.get_plugin_class_instance()
i... |
angr__angr-2558 | ReachingDefinitionsAnalysis ListIndexError
<!--
*Disclaimer:
The angr suite is maintained by a small team of volunteers.
While we cannot guarantee any timeliness for fixes and enhancements, we will do our best.
For more real-time help with angr, from us and the community, join our [Slack.](http://angr.io/invite/)*
... | [
{
"content": "from typing import Optional, Iterable, Set, Union, TYPE_CHECKING\nimport logging\n\nimport pyvex\n\nfrom ...engines.light import SimEngineLight, SimEngineLightVEXMixin, SpOffset, RegisterOffset\nfrom ...engines.vex.claripy.irop import operations as vex_operations\nfrom ...errors import SimEngineEr... | [
{
"content": "from typing import Optional, Iterable, Set, Union, TYPE_CHECKING\nimport logging\n\nimport pyvex\n\nfrom ...engines.light import SimEngineLight, SimEngineLightVEXMixin, SpOffset, RegisterOffset\nfrom ...engines.vex.claripy.irop import operations as vex_operations\nfrom ...errors import SimEngineEr... | diff --git a/angr/analyses/reaching_definitions/engine_vex.py b/angr/analyses/reaching_definitions/engine_vex.py
index 3e40607aa83..23cd60fdc7a 100644
--- a/angr/analyses/reaching_definitions/engine_vex.py
+++ b/angr/analyses/reaching_definitions/engine_vex.py
@@ -182,7 +182,7 @@ def _store_core(self, addr: Iterable[Un... |
freedomofpress__securedrop-6659 | `i18n_tool.py update-from-weblate` no longer detects translators for attribution
## Description
After #6580, including with the fix proposed in #6629, `i18n_tool.py update-from-weblate` fails to detect the translators to credit for each per-language commit.
## Steps to Reproduce
```sh-session
user@sd-dev:~/se... | [
{
"content": "#!/opt/venvs/securedrop-app-code/bin/python\n# -*- coding: utf-8 -*-\n\nimport argparse\nimport glob\nimport io\nimport json\nimport logging\nimport os\nimport re\nimport signal\nimport subprocess\nimport sys\nimport textwrap\nfrom argparse import _SubParsersAction\nfrom os.path import abspath, di... | [
{
"content": "#!/opt/venvs/securedrop-app-code/bin/python\n# -*- coding: utf-8 -*-\n\nimport argparse\nimport glob\nimport io\nimport json\nimport logging\nimport os\nimport re\nimport signal\nimport subprocess\nimport sys\nimport textwrap\nfrom argparse import _SubParsersAction\nfrom os.path import abspath, di... | diff --git a/securedrop/i18n_tool.py b/securedrop/i18n_tool.py
index 15afeb79a5..c66f8a2671 100755
--- a/securedrop/i18n_tool.py
+++ b/securedrop/i18n_tool.py
@@ -389,7 +389,10 @@ def translators(
log_command.extend([args.target, "--", path])
- log_lines = subprocess.check_output(log_command, encodi... |
pulp__pulpcore-2566 | Implementation of does_batch in DeclarativeContent is wrong
**Describe the bug**
`does_batch` is supposed to decide, whether content can be waited on, or must be evaluated asap. It produces `False` unnecessarily often, leading to performance issues.
**Additional context**
This is suspected to at least one of the r... | [
{
"content": "from gettext import gettext as _\n\nimport asyncio\n\nfrom pulpcore.constants import ALL_KNOWN_CONTENT_CHECKSUMS\nfrom pulpcore.plugin.models import Artifact\n\n\nclass DeclarativeArtifact:\n \"\"\"\n Relates an :class:`~pulpcore.plugin.models.Artifact`, how to download it, and its\n `rel... | [
{
"content": "from gettext import gettext as _\n\nimport asyncio\n\nfrom pulpcore.constants import ALL_KNOWN_CONTENT_CHECKSUMS\nfrom pulpcore.plugin.models import Artifact\n\n\nclass DeclarativeArtifact:\n \"\"\"\n Relates an :class:`~pulpcore.plugin.models.Artifact`, how to download it, and its\n `rel... | diff --git a/CHANGES/2557.bugfix b/CHANGES/2557.bugfix
new file mode 100644
index 0000000000..097b40ae8a
--- /dev/null
+++ b/CHANGES/2557.bugfix
@@ -0,0 +1 @@
+Fixed `does_batch` method in sync pipeline to allow waiting on content that is already resolved.
diff --git a/pulpcore/plugin/stages/models.py b/pulpcore/plugin... |
nextcloud__appstore-270 | API: changing the certificate for an app should delete all of its releases
Since they can not be validated anymore
| [
{
"content": "import datetime\nfrom functools import reduce\nfrom semantic_version import Version, Spec\nfrom django.conf import settings # type: ignore\nfrom django.contrib.auth.models import User # type: ignore\nfrom django.utils import timezone\nfrom django.utils.translation import ugettext_lazy as _ # ty... | [
{
"content": "import datetime\nfrom functools import reduce\nfrom semantic_version import Version, Spec\nfrom django.conf import settings # type: ignore\nfrom django.contrib.auth.models import User # type: ignore\nfrom django.utils import timezone\nfrom django.utils.translation import ugettext_lazy as _ # ty... | diff --git a/nextcloudappstore/core/models.py b/nextcloudappstore/core/models.py
index 44bc80bef30..a9ba88d2c91 100644
--- a/nextcloudappstore/core/models.py
+++ b/nextcloudappstore/core/models.py
@@ -203,6 +203,17 @@ def _latest(self, releases):
except ValueError:
return None
+ def save(self... |
wemake-services__wemake-python-styleguide-1962 | WPS503 on for ... else
### What's wrong
wps reports a useless `else` clause after a `for`, but I don't think the clause is useless: I don't see any way to remove it without writing *more* code:
```python
for search_path in self.environment.loader.searchpath:
template_relative_path = Path(search_path) / rela... | [
{
"content": "import ast\nfrom collections import defaultdict\nfrom functools import reduce\nfrom typing import ClassVar, DefaultDict, List, Mapping, Set, Type\n\nfrom typing_extensions import final\n\nfrom wemake_python_styleguide.logic import source, walk\nfrom wemake_python_styleguide.logic.tree import ifs, ... | [
{
"content": "import ast\nfrom collections import defaultdict\nfrom functools import reduce\nfrom typing import ClassVar, DefaultDict, List, Mapping, Set, Type\n\nfrom typing_extensions import final\n\nfrom wemake_python_styleguide.logic import source, walk\nfrom wemake_python_styleguide.logic.tree import ifs, ... | diff --git a/CHANGELOG.md b/CHANGELOG.md
index aba8b3a71..d468ff8ce 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -15,6 +15,13 @@ Semantic versioning in our case means:
There are no major releases right now: we are still at `0.x.y` version.
But, in the future we might change the configuration names / logic,
... |
scrapy__scrapy-4074 | Support pathlib.Path in FEED_URI
Make things work the same when the value assigned to the `FEED_URI` setting is a string containing a path or an instance of `pathlib.Path`.
| [
{
"content": "\"\"\"\nFeed Exports extension\n\nSee documentation in docs/topics/feed-exports.rst\n\"\"\"\n\nimport os\nimport sys\nimport logging\nimport posixpath\nfrom tempfile import NamedTemporaryFile\nfrom datetime import datetime\nimport six\nfrom six.moves.urllib.parse import urlparse, unquote\nfrom ftp... | [
{
"content": "\"\"\"\nFeed Exports extension\n\nSee documentation in docs/topics/feed-exports.rst\n\"\"\"\n\nimport os\nimport sys\nimport logging\nimport posixpath\nfrom tempfile import NamedTemporaryFile\nfrom datetime import datetime\nimport six\nfrom six.moves.urllib.parse import urlparse, unquote\nfrom ftp... | diff --git a/requirements-py2.txt b/requirements-py2.txt
deleted file mode 100644
index dde8d1c9c86..00000000000
--- a/requirements-py2.txt
+++ /dev/null
@@ -1,18 +0,0 @@
-parsel>=1.5.0
-PyDispatcher>=2.0.5
-w3lib>=1.17.0
-protego>=0.1.15
-
-pyOpenSSL>=16.2.0 # Earlier versions fail with "AttributeError: module 'lib' ... |
fedora-infra__bodhi-3945 | bodhi allows maintainer to request testing after stable already requested
This may be related to configuration around when rawhide gating was first enabled, but there were/are 17 cases of updates where bodhi requested stable (and indeed did tag them for stable), but the maintainer then came and requested testing. This ... | [
{
"content": "# Copyright © 2014-2019 Red Hat, Inc. and others.\n#\n# This file is part of Bodhi.\n#\n# This program is free software; you can redistribute it and/or\n# modify it under the terms of the GNU General Public License\n# as published by the Free Software Foundation; either version 2\n# of the License... | [
{
"content": "# Copyright © 2014-2019 Red Hat, Inc. and others.\n#\n# This file is part of Bodhi.\n#\n# This program is free software; you can redistribute it and/or\n# modify it under the terms of the GNU General Public License\n# as published by the Free Software Foundation; either version 2\n# of the License... | diff --git a/bodhi/server/services/updates.py b/bodhi/server/services/updates.py
index e967e83b64..405b671c9f 100644
--- a/bodhi/server/services/updates.py
+++ b/bodhi/server/services/updates.py
@@ -205,6 +205,11 @@ def set_request(request):
"Can't change request for an archived release")
... |
spack__spack-40298 | Installation issue: yaksa@0.2 +cuda %gcc@12.3.0
### Steps to reproduce the issue
I ran into this when trying to build `mpich %gcc@12.3.0 +cuda netmod=ucx pmi=pmix ^cuda@11.8 +allow-unsupported-compilers`
```console
$ spack spec -l mpich%gcc@12.3.0+cuda netmod=ucx pmi=pmix ^cuda@11.8
Input spec
------------------... | [
{
"content": "# Copyright 2013-2023 Lawrence Livermore National Security, LLC and other\n# Spack Project Developers. See the top-level COPYRIGHT file for details.\n#\n# SPDX-License-Identifier: (Apache-2.0 OR MIT)\n\nfrom spack.package import *\n\n\nclass Yaksa(AutotoolsPackage, CudaPackage, ROCmPackage):\n ... | [
{
"content": "# Copyright 2013-2023 Lawrence Livermore National Security, LLC and other\n# Spack Project Developers. See the top-level COPYRIGHT file for details.\n#\n# SPDX-License-Identifier: (Apache-2.0 OR MIT)\n\nfrom spack.package import *\n\n\nclass Yaksa(AutotoolsPackage, CudaPackage, ROCmPackage):\n ... | diff --git a/var/spack/repos/builtin/packages/yaksa/package.py b/var/spack/repos/builtin/packages/yaksa/package.py
index b696c00e1263a6..46366659be0ffc 100644
--- a/var/spack/repos/builtin/packages/yaksa/package.py
+++ b/var/spack/repos/builtin/packages/yaksa/package.py
@@ -47,6 +47,8 @@ def configure_args(self):
... |
scrapy__scrapy-4816 | Wrong annotations in scrapy.crawler.CrawlProcess.crawl docstrings
<!--
Thanks for taking an interest in Scrapy!
If you have a question that starts with "How to...", please see the Scrapy Community page: https://scrapy.org/community/.
The GitHub issue tracker's purpose is to deal with bug reports and feature requ... | [
{
"content": "import logging\nimport pprint\nimport signal\nimport warnings\n\nfrom twisted.internet import defer\nfrom zope.interface.exceptions import DoesNotImplement\n\ntry:\n # zope >= 5.0 only supports MultipleInvalid\n from zope.interface.exceptions import MultipleInvalid\nexcept ImportError:\n ... | [
{
"content": "import logging\nimport pprint\nimport signal\nimport warnings\n\nfrom twisted.internet import defer\nfrom zope.interface.exceptions import DoesNotImplement\n\ntry:\n # zope >= 5.0 only supports MultipleInvalid\n from zope.interface.exceptions import MultipleInvalid\nexcept ImportError:\n ... | diff --git a/scrapy/crawler.py b/scrapy/crawler.py
index 4c6b0e496e8..5780165360a 100644
--- a/scrapy/crawler.py
+++ b/scrapy/crawler.py
@@ -180,9 +180,9 @@ def crawl(self, crawler_or_spidercls, *args, **kwargs):
:type crawler_or_spidercls: :class:`~scrapy.crawler.Crawler` instance,
:class:`~scrap... |
RedHatInsights__insights-core-1624 | ifcfg parser to support for MASTER and TEAM_MASTER keys slave type
We need to update the ifcfg parser to support TEAMING and BONDING slave type in the configuration file, so, that we can use MASTER' and 'TEAM_MASTER' keys in raw format.
For ex- `obj['MASTER']="'bond0'"` or `obj['TEAM_MASTER']="'team0'"`
| [
{
"content": "\"\"\"\nIfCFG - files ``/etc/sysconfig/network-scripts/ifcfg-*``\n========================================================\n\nIfCFG is a parser for the network interface definition files in\n``/etc/sysconfig/network-scripts``. These are pulled into the network\nscripts using ``source``, so they a... | [
{
"content": "\"\"\"\nIfCFG - files ``/etc/sysconfig/network-scripts/ifcfg-*``\n========================================================\n\nIfCFG is a parser for the network interface definition files in\n``/etc/sysconfig/network-scripts``. These are pulled into the network\nscripts using ``source``, so they a... | diff --git a/insights/parsers/ifcfg.py b/insights/parsers/ifcfg.py
index 4abb3050fb..fc6ef1a0f7 100644
--- a/insights/parsers/ifcfg.py
+++ b/insights/parsers/ifcfg.py
@@ -95,7 +95,7 @@ def parse_content(self, content):
# In some cases we want to know what the actual value-side
# of the key i... |
Mailu__Mailu-2654 | plus mail in auto-forward destination
The email address in `admin -> User settings -> Auto-forward -> Destination` cannot contains a '+' character (error: Invalid email address). But the '+' in email address is valid and admin should accept it.
| [
{
"content": "from wtforms import validators, fields, widgets\nfrom wtforms_components import fields as fields_\nfrom flask_babel import lazy_gettext as _\n\nimport flask_login\nimport flask_wtf\nimport re\n\nLOCALPART_REGEX = \"^[a-zA-Z0-9!#$%&'*+/=?^_`{|}~-]+(?:\\.[a-zA-Z0-9!#$%&'*+/=?^_`{|}~-]+)*$\"\n\nclass... | [
{
"content": "from wtforms import validators, fields, widgets\nfrom wtforms_components import fields as fields_\nfrom flask_babel import lazy_gettext as _\n\nimport flask_login\nimport flask_wtf\nimport re\n\nLOCALPART_REGEX = \"^[a-zA-Z0-9!#$%&'*+/=?^_`{|}~-]+(?:\\.[a-zA-Z0-9!#$%&'*+/=?^_`{|}~-]+)*$\"\n\nclass... | diff --git a/core/admin/mailu/ui/forms.py b/core/admin/mailu/ui/forms.py
index 24d6f8991..587faf0e4 100644
--- a/core/admin/mailu/ui/forms.py
+++ b/core/admin/mailu/ui/forms.py
@@ -37,7 +37,7 @@ def __init__(self,message=_('Invalid email address.')):
self.message = message
def __call__(self, form, field... |
electricitymaps__electricitymaps-contrib-982 | Mockserver server.py does not work in Python 3
```mockserver_1 | Traceback (most recent call last):
mockserver_1 | File "../server.py", line 5, in <module>
mockserver_1 | from SimpleHTTPServer import SimpleHTTPRequestHandler, BaseHTTPServer
mockserver_1 | ModuleNotFoundError: No module named 'SimpleHTTPSer... | [
{
"content": "#! /usr/bin/env python\n\n# Usage: python __file__.py <port>\n\nfrom SimpleHTTPServer import SimpleHTTPRequestHandler, BaseHTTPServer\n\nclass CORSRequestHandler(SimpleHTTPRequestHandler):\n def do_OPTIONS(self):\n self.send_response(200, 'OK')\n self.end_headers()\n\n def end_... | [
{
"content": "#! /usr/bin/env python\n\n# Usage: python __file__.py <port>\n\nfrom http.server import SimpleHTTPRequestHandler, HTTPServer, test\n\nclass CORSRequestHandler(SimpleHTTPRequestHandler):\n def do_OPTIONS(self):\n self.send_response(200, 'OK')\n self.end_headers()\n\n def end_hea... | diff --git a/docker-compose.yml b/docker-compose.yml
index 42eccd5251..d59ef21b7e 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -2,7 +2,7 @@ version: '3.2'
services:
mockserver:
build: mockserver
- ports: ['9000:9000']
+ ports: ['9000:8000']
volumes:
- './mockserver/public:/home... |
Qiskit__qiskit-11782 | Round durations in `GenericBackendV2`
<!--
⚠️ If you do not respect this template, your pull request will be closed.
⚠️ Your pull request title should be short detailed and understandable for all.
⚠️ Also, please add a release note file using reno if the change needs to be
documented in the release notes.
⚠️ If ... | [
{
"content": "# This code is part of Qiskit.\n#\n# (C) Copyright IBM 2020.\n#\n# This code is licensed under the Apache License, Version 2.0. You may\n# obtain a copy of this license in the LICENSE.txt file in the root directory\n# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.\n#\n# Any ... | [
{
"content": "# This code is part of Qiskit.\n#\n# (C) Copyright IBM 2020.\n#\n# This code is licensed under the Apache License, Version 2.0. You may\n# obtain a copy of this license in the LICENSE.txt file in the root directory\n# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.\n#\n# Any ... | diff --git a/qiskit/circuit/duration.py b/qiskit/circuit/duration.py
index 88ce7af968ed..6acb230baadd 100644
--- a/qiskit/circuit/duration.py
+++ b/qiskit/circuit/duration.py
@@ -78,8 +78,15 @@ def convert_durations_to_dt(qc: QuantumCircuit, dt_in_sec: float, inplace=True):
operation.duration = duration_in_dt(... |
prowler-cloud__prowler-2430 | [Bug]: cloudfront_distributions_https_enabled false positive
### Steps to Reproduce
Prowler reports cloudfront_distributions_https_enabled as failed when the cloudfront distribution has "Redirect HTTP to HTTPS" set in viewer protocol policy on behavior.
### Expected behavior
For the example attached, it should "PAS... | [
{
"content": "from dataclasses import dataclass\nfrom enum import Enum\nfrom typing import Optional\n\nfrom pydantic import BaseModel\n\nfrom prowler.lib.logger import logger\nfrom prowler.lib.scan_filters.scan_filters import is_resource_filtered\nfrom prowler.providers.aws.aws_provider import generate_regional... | [
{
"content": "from enum import Enum\nfrom typing import Optional\n\nfrom pydantic import BaseModel\n\nfrom prowler.lib.logger import logger\nfrom prowler.lib.scan_filters.scan_filters import is_resource_filtered\nfrom prowler.providers.aws.aws_provider import generate_regional_clients\n\n\n################## Cl... | diff --git a/prowler/providers/aws/services/cloudfront/cloudfront_service.py b/prowler/providers/aws/services/cloudfront/cloudfront_service.py
index cbd1a18d80..8c51a31a71 100644
--- a/prowler/providers/aws/services/cloudfront/cloudfront_service.py
+++ b/prowler/providers/aws/services/cloudfront/cloudfront_service.py
@... |
ray-project__ray-9461 | [tune] ExperimentAnalysis doesn't expand user as docs imply
### What is the problem?
The Ray docs give an example where a path with the "~" operator is included:
https://docs.ray.io/en/master/tune/api_docs/analysis.html#experimentanalysis-tune-experimentanalysis
```
>>> tune.run(my_trainable, name="my_exp", loc... | [
{
"content": "import json\nimport logging\nimport os\n\ntry:\n import pandas as pd\nexcept ImportError:\n pd = None\n\nfrom ray.tune.error import TuneError\nfrom ray.tune.result import EXPR_PROGRESS_FILE, EXPR_PARAM_FILE,\\\n CONFIG_PREFIX, TRAINING_ITERATION\nfrom ray.tune.trial import Trial\nfrom ray... | [
{
"content": "import json\nimport logging\nimport os\n\ntry:\n import pandas as pd\nexcept ImportError:\n pd = None\n\nfrom ray.tune.error import TuneError\nfrom ray.tune.result import EXPR_PROGRESS_FILE, EXPR_PARAM_FILE,\\\n CONFIG_PREFIX, TRAINING_ITERATION\nfrom ray.tune.trial import Trial\nfrom ray... | diff --git a/python/ray/tune/analysis/experiment_analysis.py b/python/ray/tune/analysis/experiment_analysis.py
index 12023a3779052..09aba20a619b3 100644
--- a/python/ray/tune/analysis/experiment_analysis.py
+++ b/python/ray/tune/analysis/experiment_analysis.py
@@ -201,6 +201,11 @@ class ExperimentAnalysis(Analysis):
... |
beetbox__beets-4974 | discogs: TypeError whenever artist and title are empty
<!--
Describe your problem, feature request, or discussion topic here.
Please fill out this and the "Setup" section below and remember to include
enough detail so that other people can reproduce the problem.
-->
### Problem
Running this command in verbo... | [
{
"content": "# This file is part of beets.\n# Copyright 2016, Adrian Sampson.\n#\n# Permission is hereby granted, free of charge, to any person obtaining\n# a copy of this software and associated documentation files (the\n# \"Software\"), to deal in the Software without restriction, including\n# without limita... | [
{
"content": "# This file is part of beets.\n# Copyright 2016, Adrian Sampson.\n#\n# Permission is hereby granted, free of charge, to any person obtaining\n# a copy of this software and associated documentation files (the\n# \"Software\"), to deal in the Software without restriction, including\n# without limita... | diff --git a/beetsplug/discogs.py b/beetsplug/discogs.py
index 5bdd277054..5085214f52 100644
--- a/beetsplug/discogs.py
+++ b/beetsplug/discogs.py
@@ -246,13 +246,13 @@ def item_candidates(self, item, artist, title):
:rtype: list[beets.autotag.hooks.TrackInfo]
"""
if not self.discogs_client:
... |
speechbrain__speechbrain-1929 | [Bug]: Error while running the TransducerLoss module
### Describe the bug
Originally reported by #1867
`ValueError: Found inputs tensors to be on [device(type='cuda', index=0), device(type='cuda', index=0), device(type='cuda', index=0), device(type='cuda', index=0)] while needed to be on a 'cuda' device to use th... | [
{
"content": "\"\"\"\nTransducer loss implementation (depends on numba)\n\nAuthors\n * Abdelwahab Heba 2020\n\"\"\"\n\nimport torch\nfrom torch.autograd import Function\nfrom torch.nn import Module\n\ntry:\n from numba import cuda\nexcept ImportError:\n err_msg = \"The optional dependency Numba is needed ... | [
{
"content": "\"\"\"\nTransducer loss implementation (depends on numba)\n\nAuthors\n * Abdelwahab Heba 2020\n\"\"\"\n\nimport torch\nfrom torch.autograd import Function\nfrom torch.nn import Module\n\ntry:\n from numba import cuda\nexcept ImportError:\n err_msg = \"The optional dependency Numba is needed ... | diff --git a/speechbrain/nnet/loss/transducer_loss.py b/speechbrain/nnet/loss/transducer_loss.py
index cc15a1ace3..39317daf9a 100644
--- a/speechbrain/nnet/loss/transducer_loss.py
+++ b/speechbrain/nnet/loss/transducer_loss.py
@@ -340,7 +340,7 @@ def __init__(self, blank=0, reduction="mean"):
def forward(self, log... |
open-mmlab__mmdetection-1935 | CUDA error: device-side assert triggered
The error is related to learning_rate, I want to use 4 gpus and each gpu has 24 images to train ssd, so according to your markdown, I should set lr = 3e-3, but CUDA error occurs, if I decreasing the lr, the problem is fixed, so what's the problem?
| [
{
"content": "import numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmcv.cnn import xavier_init\n\nfrom mmdet.core import AnchorGenerator, anchor_target, multi_apply\nfrom ..losses import smooth_l1_loss\nfrom ..registry import HEADS\nfrom .anchor_head import AnchorHead\n... | [
{
"content": "import numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmcv.cnn import xavier_init\n\nfrom mmdet.core import AnchorGenerator, anchor_target, multi_apply\nfrom ..losses import smooth_l1_loss\nfrom ..registry import HEADS\nfrom .anchor_head import AnchorHead\n... | diff --git a/mmdet/models/anchor_heads/ssd_head.py b/mmdet/models/anchor_heads/ssd_head.py
index 056dfa53f25..57113679b45 100644
--- a/mmdet/models/anchor_heads/ssd_head.py
+++ b/mmdet/models/anchor_heads/ssd_head.py
@@ -182,6 +182,12 @@ def loss(self,
all_bbox_weights = torch.cat(bbox_weights_list,
... |
locustio__locust-2493 | FastHttpUser is stricter than HttpUser about the host attribute
### Prerequisites
- [X] I am using [the latest version of Locust](https://github.com/locustio/locust/releases/)
- [X] I am reporting a bug, not asking a question
### Description
As a followup to https://github.com/locustio/locust/issues/1222:
`FastHt... | [
{
"content": "from __future__ import annotations\nimport re\nimport socket\nimport json\nimport json as unshadowed_json # some methods take a named parameter called json\nfrom base64 import b64encode\nfrom contextlib import contextmanager\nfrom json.decoder import JSONDecodeError\nfrom urllib.parse import urlp... | [
{
"content": "from __future__ import annotations\nimport re\nimport socket\nimport json\nimport json as unshadowed_json # some methods take a named parameter called json\nfrom base64 import b64encode\nfrom contextlib import contextmanager\nfrom json.decoder import JSONDecodeError\nfrom urllib.parse import urlp... | diff --git a/locust/contrib/fasthttp.py b/locust/contrib/fasthttp.py
index 73dff3551b..912023d121 100644
--- a/locust/contrib/fasthttp.py
+++ b/locust/contrib/fasthttp.py
@@ -338,8 +338,6 @@ def __init__(self, environment):
raise LocustError(
"You must specify the base host. Either in the ... |
mlflow__mlflow-3929 | [BUG] archive_existing_versions flag not working
Thank you for submitting an issue. Please refer to our [issue policy](https://www.github.com/mlflow/mlflow/blob/master/ISSUE_POLICY.md) for additional information about bug reports. For help with debugging your code, please refer to [Stack Overflow](https://stackoverflow... | [
{
"content": "import time\n\nimport logging\nimport sqlalchemy\n\nfrom mlflow.entities.model_registry.model_version_stages import (\n get_canonical_stage,\n DEFAULT_STAGES_FOR_GET_LATEST_VERSIONS,\n STAGE_DELETED_INTERNAL,\n STAGE_ARCHIVED,\n)\nfrom mlflow.exceptions import MlflowException\nfrom mlf... | [
{
"content": "import time\n\nimport logging\nimport sqlalchemy\n\nfrom mlflow.entities.model_registry.model_version_stages import (\n get_canonical_stage,\n DEFAULT_STAGES_FOR_GET_LATEST_VERSIONS,\n STAGE_DELETED_INTERNAL,\n STAGE_ARCHIVED,\n)\nfrom mlflow.exceptions import MlflowException\nfrom mlf... | diff --git a/mlflow/store/model_registry/sqlalchemy_store.py b/mlflow/store/model_registry/sqlalchemy_store.py
index aaa2f79f2ffad..b6fee936ed8d1 100644
--- a/mlflow/store/model_registry/sqlalchemy_store.py
+++ b/mlflow/store/model_registry/sqlalchemy_store.py
@@ -662,7 +662,7 @@ def transition_model_version_stage(self... |
svthalia__concrexit-1628 | Warnings about DEFAULT_AUTO_FIELD setting since Django 3.2
<!--
Please add the appropriate label for what change should be made:
docs: changes to the documentation)
refactor: refactoring production code, eg. renaming a variable or rewriting a function
test: adding missing tests, refactoring tests; no production c... | [
{
"content": "\"\"\"Django settings for concrexit.\n\nFor more information on this file, see\nhttps://docs.djangoproject.com/en/dev/topics/settings/\n\nFor the full list of settings and their values, see\nhttps://docs.djangoproject.com/en/dev/ref/settings/\n\"\"\"\n\nimport logging\n\nimport base64\nimport json... | [
{
"content": "\"\"\"Django settings for concrexit.\n\nFor more information on this file, see\nhttps://docs.djangoproject.com/en/dev/topics/settings/\n\nFor the full list of settings and their values, see\nhttps://docs.djangoproject.com/en/dev/ref/settings/\n\"\"\"\n\nimport logging\n\nimport base64\nimport json... | diff --git a/website/thaliawebsite/settings.py b/website/thaliawebsite/settings.py
index c7aa7f4f7..ce4436d1c 100644
--- a/website/thaliawebsite/settings.py
+++ b/website/thaliawebsite/settings.py
@@ -228,6 +228,9 @@ def from_env(
# https://docs.djangoproject.com/en/dev/ref/settings/#csrf-cookie-secure
CSRF_COOKIE_SE... |
oppia__oppia-10876 | Skill ids are not viewable in My Contributions > Skills
<!--
- Thanks for taking the time to report a bug in the Oppia project.
- Before filing a new issue, please do a quick search to check that it hasn't
- already been filed on the [issue tracker](https://github.com/oppia/oppia/issues)._
-->
**Describe... | [
{
"content": "# coding: utf-8\n#\n# Copyright 2019 The Oppia Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licen... | [
{
"content": "# coding: utf-8\n#\n# Copyright 2019 The Oppia Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licen... | diff --git a/core/domain/opportunity_services.py b/core/domain/opportunity_services.py
index 3651f6ef22af7..b57e53f657326 100644
--- a/core/domain/opportunity_services.py
+++ b/core/domain/opportunity_services.py
@@ -623,10 +623,9 @@ def get_skill_opportunities_by_ids(ids):
opportunity_models.SkillOpportunityM... |
electricitymaps__electricitymaps-contrib-1060 | MISO <-> Canada interconnector
..needs to be updated as it still pointing to Montana instead of MISO
| [
{
"content": "#!/usr/bin/env python3\n\n# The arrow library is used to handle datetimes\nimport arrow\n# The request library is used to fetch content through HTTP\nimport requests\n\nfrom bs4 import BeautifulSoup\n\nMAP_GENERATION = {\n 'BIOFUEL': 'biomass',\n 'GAS': 'gas',\n 'HYDRO': 'hydro',\n 'NU... | [
{
"content": "#!/usr/bin/env python3\n\n# The arrow library is used to handle datetimes\nimport arrow\n# The request library is used to fetch content through HTTP\nimport requests\n\nfrom bs4 import BeautifulSoup\n\nMAP_GENERATION = {\n 'BIOFUEL': 'biomass',\n 'GAS': 'gas',\n 'HYDRO': 'hydro',\n 'NU... | diff --git a/config/exchanges.json b/config/exchanges.json
index f34be3198c..d69a58d996 100644
--- a/config/exchanges.json
+++ b/config/exchanges.json
@@ -405,25 +405,15 @@
},
"rotation": 90
},
- "CA-ON->US-MI": {
+ "CA-ON->US-MISO": {
"lonlat": [
- -82.1195,
- 43.5896
+ -84.188,
+ ... |
ckan__ckan-5864 | Embed file in another site problem with mime type
**CKAN version**
ckan_version": "2.9"
**Describe the bug**
Embed file in another site from ckan force to download because mime type is incorrect, always is embed with Content-Type: application/octet-stream
**Steps to reproduce**
Load PDF in ckan and use embed t... | [
{
"content": "# encoding: utf-8\nimport cgi\nimport json\nimport logging\n\nimport flask\nfrom flask.views import MethodView\n\nimport six\nimport ckan.lib.base as base\nimport ckan.lib.datapreview as lib_datapreview\nimport ckan.lib.helpers as h\nimport ckan.lib.navl.dictization_functions as dict_fns\nimport c... | [
{
"content": "# encoding: utf-8\nimport cgi\nimport json\nimport logging\n\nimport flask\nfrom flask.views import MethodView\n\nimport six\nimport ckan.lib.base as base\nimport ckan.lib.datapreview as lib_datapreview\nimport ckan.lib.helpers as h\nimport ckan.lib.navl.dictization_functions as dict_fns\nimport c... | diff --git a/ckan/tests/controllers/test_package.py b/ckan/tests/controllers/test_package.py
index 6107a828d19..c7025ddf566 100644
--- a/ckan/tests/controllers/test_package.py
+++ b/ckan/tests/controllers/test_package.py
@@ -1065,6 +1065,29 @@ def test_anonymous_users_cannot_edit_resource(self, app):
)
... |
python__mypy-9462 | TypeVar with Any upper bound generates missing attribute errors is used as Type[T]
Imagine the following code:
```
class Foo():
NAME: ClassVar[str] = ""
T = TypeVar("T", bound=Foo)
def create_foo(object_class: Type[T]) -> T:
print(T.NAME) # Generates error in static analysis
return T()
```
... | [
{
"content": "\"\"\"Type checking of attribute access\"\"\"\n\nfrom typing import cast, Callable, Optional, Union, Sequence\nfrom typing_extensions import TYPE_CHECKING\n\nfrom mypy.types import (\n Type, Instance, AnyType, TupleType, TypedDictType, CallableType, FunctionLike,\n TypeVarLikeDef, Overloaded... | [
{
"content": "\"\"\"Type checking of attribute access\"\"\"\n\nfrom typing import cast, Callable, Optional, Union, Sequence\nfrom typing_extensions import TYPE_CHECKING\n\nfrom mypy.types import (\n Type, Instance, AnyType, TupleType, TypedDictType, CallableType, FunctionLike,\n TypeVarLikeDef, Overloaded... | diff --git a/mypy/checkmember.py b/mypy/checkmember.py
index b9221958dacb..64e693d52c96 100644
--- a/mypy/checkmember.py
+++ b/mypy/checkmember.py
@@ -280,6 +280,9 @@ def analyze_type_type_member_access(name: str,
item = upper_bound
elif isinstance(upper_bound, TupleType):
item = tupl... |
MycroftAI__mycroft-core-2338 | Skill Shutdown sometimes fails causing duplicate instances of Skill
## Devices:
Issue occurred on two separate Debian Linux installations
- one running Mycroft-core 19.02.14,
- the other running dev branch the date of this issue.
## Steps to reproduce:
1. Load Mycroft and ensure working as expected
2. Make int... | [
{
"content": "from inspect import signature\n\nfrom mycroft.messagebus.message import Message\nfrom mycroft.metrics import Stopwatch, report_timing\nfrom mycroft.util.log import LOG\n\nfrom ..skill_data import to_alnum\n\n\ndef unmunge_message(message, skill_id):\n \"\"\"Restore message keywords by removing ... | [
{
"content": "from inspect import signature\n\nfrom mycroft.messagebus.message import Message\nfrom mycroft.metrics import Stopwatch, report_timing\nfrom mycroft.util.log import LOG\n\nfrom ..skill_data import to_alnum\n\n\ndef unmunge_message(message, skill_id):\n \"\"\"Restore message keywords by removing ... | diff --git a/mycroft/skills/mycroft_skill/event_container.py b/mycroft/skills/mycroft_skill/event_container.py
index fb444d005af0..623ae3f602d0 100644
--- a/mycroft/skills/mycroft_skill/event_container.py
+++ b/mycroft/skills/mycroft_skill/event_container.py
@@ -138,9 +138,12 @@ def once_wrapper(message):
if h... |
huggingface__trl-743 | Bug in dpo_trainer.py, prediction_step function
I see the following code in dpo_trainer.py's prediction_step method:
```python
... ...
logits_dict = {
"logits_test/chosen": metrics["logits_test/chosen"],
"logits_test/rejected": metrics["logits_test/rejected"],
}
... ...
```
But the keys and values are ... | [
{
"content": "# DPO Authors: Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, and Chelsea Finn 2023\n# Copyright 2023 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in com... | [
{
"content": "# DPO Authors: Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, and Chelsea Finn 2023\n# Copyright 2023 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in com... | diff --git a/trl/trainer/dpo_trainer.py b/trl/trainer/dpo_trainer.py
index c30e2b6c2e..3f4ee1503b 100644
--- a/trl/trainer/dpo_trainer.py
+++ b/trl/trainer/dpo_trainer.py
@@ -472,8 +472,8 @@ def prediction_step(
# logits for the chosen and rejected samples from model
logits_dict = {
- "lo... |
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